Overview

Dataset statistics

Number of variables369
Number of observations191652
Missing cells13864522
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory539.5 MiB
Average record size in memory2.9 KiB

Variable types

Numeric201
Categorical168

Alerts

EINGEFUEGT_AM has a high cardinality: 3034 distinct values High cardinality
AKT_DAT_KL has 46596 (24.3%) missing values Missing
ALTER_HH has 46596 (24.3%) missing values Missing
ALTER_KIND1 has 179886 (93.9%) missing values Missing
ALTER_KIND2 has 186552 (97.3%) missing values Missing
ALTER_KIND3 has 190377 (99.3%) missing values Missing
ALTER_KIND4 has 191416 (99.9%) missing values Missing
ALTERSKATEGORIE_FEIN has 51842 (27.1%) missing values Missing
ANZ_HAUSHALTE_AKTIV has 49927 (26.1%) missing values Missing
ANZ_HH_TITEL has 52110 (27.2%) missing values Missing
ANZ_KINDER has 46596 (24.3%) missing values Missing
ANZ_PERSONEN has 46596 (24.3%) missing values Missing
ANZ_STATISTISCHE_HAUSHALTE has 49927 (26.1%) missing values Missing
ANZ_TITEL has 46596 (24.3%) missing values Missing
ARBEIT has 50476 (26.3%) missing values Missing
BALLRAUM has 49959 (26.1%) missing values Missing
CAMEO_DEU_2015 has 50428 (26.3%) missing values Missing
CAMEO_DEUG_2015 has 50428 (26.3%) missing values Missing
CAMEO_INTL_2015 has 50428 (26.3%) missing values Missing
CJT_GESAMTTYP has 3213 (1.7%) missing values Missing
CJT_KATALOGNUTZER has 3213 (1.7%) missing values Missing
CJT_TYP_1 has 3213 (1.7%) missing values Missing
CJT_TYP_2 has 3213 (1.7%) missing values Missing
CJT_TYP_3 has 3213 (1.7%) missing values Missing
CJT_TYP_4 has 3213 (1.7%) missing values Missing
CJT_TYP_5 has 3213 (1.7%) missing values Missing
CJT_TYP_6 has 3213 (1.7%) missing values Missing
D19_BANKEN_ONLINE_QUOTE_12 has 47697 (24.9%) missing values Missing
D19_GESAMT_ONLINE_QUOTE_12 has 47697 (24.9%) missing values Missing
D19_KONSUMTYP has 47697 (24.9%) missing values Missing
D19_LETZTER_KAUF_BRANCHE has 47697 (24.9%) missing values Missing
D19_LOTTO has 47697 (24.9%) missing values Missing
D19_SOZIALES has 47697 (24.9%) missing values Missing
D19_TELKO_ONLINE_QUOTE_12 has 47697 (24.9%) missing values Missing
D19_VERSAND_ONLINE_QUOTE_12 has 47697 (24.9%) missing values Missing
D19_VERSI_ONLINE_QUOTE_12 has 47697 (24.9%) missing values Missing
DSL_FLAG has 49927 (26.1%) missing values Missing
EINGEFUEGT_AM has 49927 (26.1%) missing values Missing
EINGEZOGENAM_HH_JAHR has 46596 (24.3%) missing values Missing
EWDICHTE has 49959 (26.1%) missing values Missing
EXTSEL992 has 85283 (44.5%) missing values Missing
FIRMENDICHTE has 49927 (26.1%) missing values Missing
GEBAEUDETYP has 49927 (26.1%) missing values Missing
GEBAEUDETYP_RASTER has 49927 (26.1%) missing values Missing
GEMEINDETYP has 50476 (26.3%) missing values Missing
GFK_URLAUBERTYP has 3213 (1.7%) missing values Missing
HH_DELTA_FLAG has 53742 (28.0%) missing values Missing
HH_EINKOMMEN_SCORE has 2968 (1.5%) missing values Missing
INNENSTADT has 49959 (26.1%) missing values Missing
KBA05_ALTER1 has 55980 (29.2%) missing values Missing
KBA05_ALTER2 has 55980 (29.2%) missing values Missing
KBA05_ALTER3 has 55980 (29.2%) missing values Missing
KBA05_ALTER4 has 55980 (29.2%) missing values Missing
KBA05_ANHANG has 55980 (29.2%) missing values Missing
KBA05_ANTG1 has 55980 (29.2%) missing values Missing
KBA05_ANTG2 has 55980 (29.2%) missing values Missing
KBA05_ANTG3 has 55980 (29.2%) missing values Missing
KBA05_ANTG4 has 55980 (29.2%) missing values Missing
KBA05_AUTOQUOT has 55980 (29.2%) missing values Missing
KBA05_BAUMAX has 55980 (29.2%) missing values Missing
KBA05_CCM1 has 55980 (29.2%) missing values Missing
KBA05_CCM2 has 55980 (29.2%) missing values Missing
KBA05_CCM3 has 55980 (29.2%) missing values Missing
KBA05_CCM4 has 55980 (29.2%) missing values Missing
KBA05_DIESEL has 55980 (29.2%) missing values Missing
KBA05_FRAU has 55980 (29.2%) missing values Missing
KBA05_GBZ has 55980 (29.2%) missing values Missing
KBA05_HERST1 has 55980 (29.2%) missing values Missing
KBA05_HERST2 has 55980 (29.2%) missing values Missing
KBA05_HERST3 has 55980 (29.2%) missing values Missing
KBA05_HERST4 has 55980 (29.2%) missing values Missing
KBA05_HERST5 has 55980 (29.2%) missing values Missing
KBA05_HERSTTEMP has 49927 (26.1%) missing values Missing
KBA05_KRSAQUOT has 55980 (29.2%) missing values Missing
KBA05_KRSHERST1 has 55980 (29.2%) missing values Missing
KBA05_KRSHERST2 has 55980 (29.2%) missing values Missing
KBA05_KRSHERST3 has 55980 (29.2%) missing values Missing
KBA05_KRSKLEIN has 55980 (29.2%) missing values Missing
KBA05_KRSOBER has 55980 (29.2%) missing values Missing
KBA05_KRSVAN has 55980 (29.2%) missing values Missing
KBA05_KRSZUL has 55980 (29.2%) missing values Missing
KBA05_KW1 has 55980 (29.2%) missing values Missing
KBA05_KW2 has 55980 (29.2%) missing values Missing
KBA05_KW3 has 55980 (29.2%) missing values Missing
KBA05_MAXAH has 55980 (29.2%) missing values Missing
KBA05_MAXBJ has 55980 (29.2%) missing values Missing
KBA05_MAXHERST has 55980 (29.2%) missing values Missing
KBA05_MAXSEG has 55980 (29.2%) missing values Missing
KBA05_MAXVORB has 55980 (29.2%) missing values Missing
KBA05_MOD1 has 55980 (29.2%) missing values Missing
KBA05_MOD2 has 55980 (29.2%) missing values Missing
KBA05_MOD3 has 55980 (29.2%) missing values Missing
KBA05_MOD4 has 55980 (29.2%) missing values Missing
KBA05_MOD8 has 55980 (29.2%) missing values Missing
KBA05_MODTEMP has 49927 (26.1%) missing values Missing
KBA05_MOTOR has 55980 (29.2%) missing values Missing
KBA05_MOTRAD has 55980 (29.2%) missing values Missing
KBA05_SEG1 has 55980 (29.2%) missing values Missing
KBA05_SEG10 has 55980 (29.2%) missing values Missing
KBA05_SEG2 has 55980 (29.2%) missing values Missing
KBA05_SEG3 has 55980 (29.2%) missing values Missing
KBA05_SEG4 has 55980 (29.2%) missing values Missing
KBA05_SEG5 has 55980 (29.2%) missing values Missing
KBA05_SEG6 has 55980 (29.2%) missing values Missing
KBA05_SEG7 has 55980 (29.2%) missing values Missing
KBA05_SEG8 has 55980 (29.2%) missing values Missing
KBA05_SEG9 has 55980 (29.2%) missing values Missing
KBA05_VORB0 has 55980 (29.2%) missing values Missing
KBA05_VORB1 has 55980 (29.2%) missing values Missing
KBA05_VORB2 has 55980 (29.2%) missing values Missing
KBA05_ZUL1 has 55980 (29.2%) missing values Missing
KBA05_ZUL2 has 55980 (29.2%) missing values Missing
KBA05_ZUL3 has 55980 (29.2%) missing values Missing
KBA05_ZUL4 has 55980 (29.2%) missing values Missing
KBA13_ALTERHALTER_30 has 51281 (26.8%) missing values Missing
KBA13_ALTERHALTER_45 has 51281 (26.8%) missing values Missing
KBA13_ALTERHALTER_60 has 51281 (26.8%) missing values Missing
KBA13_ALTERHALTER_61 has 51281 (26.8%) missing values Missing
KBA13_ANTG1 has 51281 (26.8%) missing values Missing
KBA13_ANTG2 has 51281 (26.8%) missing values Missing
KBA13_ANTG3 has 51281 (26.8%) missing values Missing
KBA13_ANTG4 has 51281 (26.8%) missing values Missing
KBA13_ANZAHL_PKW has 51281 (26.8%) missing values Missing
KBA13_AUDI has 51281 (26.8%) missing values Missing
KBA13_AUTOQUOTE has 51281 (26.8%) missing values Missing
KBA13_BAUMAX has 51281 (26.8%) missing values Missing
KBA13_BJ_1999 has 51281 (26.8%) missing values Missing
KBA13_BJ_2000 has 51281 (26.8%) missing values Missing
KBA13_BJ_2004 has 51281 (26.8%) missing values Missing
KBA13_BJ_2006 has 51281 (26.8%) missing values Missing
KBA13_BJ_2008 has 51281 (26.8%) missing values Missing
KBA13_BJ_2009 has 51281 (26.8%) missing values Missing
KBA13_BMW has 51281 (26.8%) missing values Missing
KBA13_CCM_0_1400 has 51281 (26.8%) missing values Missing
KBA13_CCM_1000 has 51281 (26.8%) missing values Missing
KBA13_CCM_1200 has 51281 (26.8%) missing values Missing
KBA13_CCM_1400 has 51281 (26.8%) missing values Missing
KBA13_CCM_1401_2500 has 51281 (26.8%) missing values Missing
KBA13_CCM_1500 has 51281 (26.8%) missing values Missing
KBA13_CCM_1600 has 51281 (26.8%) missing values Missing
KBA13_CCM_1800 has 51281 (26.8%) missing values Missing
KBA13_CCM_2000 has 51281 (26.8%) missing values Missing
KBA13_CCM_2500 has 51281 (26.8%) missing values Missing
KBA13_CCM_2501 has 51281 (26.8%) missing values Missing
KBA13_CCM_3000 has 51281 (26.8%) missing values Missing
KBA13_CCM_3001 has 51281 (26.8%) missing values Missing
KBA13_FAB_ASIEN has 51281 (26.8%) missing values Missing
KBA13_FAB_SONSTIGE has 51281 (26.8%) missing values Missing
KBA13_FIAT has 51281 (26.8%) missing values Missing
KBA13_FORD has 51281 (26.8%) missing values Missing
KBA13_GBZ has 51281 (26.8%) missing values Missing
KBA13_HALTER_20 has 51281 (26.8%) missing values Missing
KBA13_HALTER_25 has 51281 (26.8%) missing values Missing
KBA13_HALTER_30 has 51281 (26.8%) missing values Missing
KBA13_HALTER_35 has 51281 (26.8%) missing values Missing
KBA13_HALTER_40 has 51281 (26.8%) missing values Missing
KBA13_HALTER_45 has 51281 (26.8%) missing values Missing
KBA13_HALTER_50 has 51281 (26.8%) missing values Missing
KBA13_HALTER_55 has 51281 (26.8%) missing values Missing
KBA13_HALTER_60 has 51281 (26.8%) missing values Missing
KBA13_HALTER_65 has 51281 (26.8%) missing values Missing
KBA13_HALTER_66 has 51281 (26.8%) missing values Missing
KBA13_HERST_ASIEN has 51281 (26.8%) missing values Missing
KBA13_HERST_AUDI_VW has 51281 (26.8%) missing values Missing
KBA13_HERST_BMW_BENZ has 51281 (26.8%) missing values Missing
KBA13_HERST_EUROPA has 51281 (26.8%) missing values Missing
KBA13_HERST_FORD_OPEL has 51281 (26.8%) missing values Missing
KBA13_HERST_SONST has 51281 (26.8%) missing values Missing
KBA13_HHZ has 51281 (26.8%) missing values Missing
KBA13_KMH_0_140 has 51281 (26.8%) missing values Missing
KBA13_KMH_110 has 51281 (26.8%) missing values Missing
KBA13_KMH_140 has 51281 (26.8%) missing values Missing
KBA13_KMH_140_210 has 51281 (26.8%) missing values Missing
KBA13_KMH_180 has 51281 (26.8%) missing values Missing
KBA13_KMH_210 has 51281 (26.8%) missing values Missing
KBA13_KMH_211 has 51281 (26.8%) missing values Missing
KBA13_KMH_250 has 51281 (26.8%) missing values Missing
KBA13_KMH_251 has 51281 (26.8%) missing values Missing
KBA13_KRSAQUOT has 51281 (26.8%) missing values Missing
KBA13_KRSHERST_AUDI_VW has 51281 (26.8%) missing values Missing
KBA13_KRSHERST_BMW_BENZ has 51281 (26.8%) missing values Missing
KBA13_KRSHERST_FORD_OPEL has 51281 (26.8%) missing values Missing
KBA13_KRSSEG_KLEIN has 51281 (26.8%) missing values Missing
KBA13_KRSSEG_OBER has 51281 (26.8%) missing values Missing
KBA13_KRSSEG_VAN has 51281 (26.8%) missing values Missing
KBA13_KRSZUL_NEU has 51281 (26.8%) missing values Missing
KBA13_KW_0_60 has 51281 (26.8%) missing values Missing
KBA13_KW_110 has 51281 (26.8%) missing values Missing
KBA13_KW_120 has 51281 (26.8%) missing values Missing
KBA13_KW_121 has 51281 (26.8%) missing values Missing
KBA13_KW_30 has 51281 (26.8%) missing values Missing
KBA13_KW_40 has 51281 (26.8%) missing values Missing
KBA13_KW_50 has 51281 (26.8%) missing values Missing
KBA13_KW_60 has 51281 (26.8%) missing values Missing
KBA13_KW_61_120 has 51281 (26.8%) missing values Missing
KBA13_KW_70 has 51281 (26.8%) missing values Missing
KBA13_KW_80 has 51281 (26.8%) missing values Missing
KBA13_KW_90 has 51281 (26.8%) missing values Missing
KBA13_MAZDA has 51281 (26.8%) missing values Missing
KBA13_MERCEDES has 51281 (26.8%) missing values Missing
KBA13_MOTOR has 51281 (26.8%) missing values Missing
KBA13_NISSAN has 51281 (26.8%) missing values Missing
KBA13_OPEL has 51281 (26.8%) missing values Missing
KBA13_PEUGEOT has 51281 (26.8%) missing values Missing
KBA13_RENAULT has 51281 (26.8%) missing values Missing
KBA13_SEG_GELAENDEWAGEN has 51281 (26.8%) missing values Missing
KBA13_SEG_GROSSRAUMVANS has 51281 (26.8%) missing values Missing
KBA13_SEG_KLEINST has 51281 (26.8%) missing values Missing
KBA13_SEG_KLEINWAGEN has 51281 (26.8%) missing values Missing
KBA13_SEG_KOMPAKTKLASSE has 51281 (26.8%) missing values Missing
KBA13_SEG_MINIVANS has 51281 (26.8%) missing values Missing
KBA13_SEG_MINIWAGEN has 51281 (26.8%) missing values Missing
KBA13_SEG_MITTELKLASSE has 51281 (26.8%) missing values Missing
KBA13_SEG_OBEREMITTELKLASSE has 51281 (26.8%) missing values Missing
KBA13_SEG_OBERKLASSE has 51281 (26.8%) missing values Missing
KBA13_SEG_SONSTIGE has 51281 (26.8%) missing values Missing
KBA13_SEG_SPORTWAGEN has 51281 (26.8%) missing values Missing
KBA13_SEG_UTILITIES has 51281 (26.8%) missing values Missing
KBA13_SEG_VAN has 51281 (26.8%) missing values Missing
KBA13_SEG_WOHNMOBILE has 51281 (26.8%) missing values Missing
KBA13_SITZE_4 has 51281 (26.8%) missing values Missing
KBA13_SITZE_5 has 51281 (26.8%) missing values Missing
KBA13_SITZE_6 has 51281 (26.8%) missing values Missing
KBA13_TOYOTA has 51281 (26.8%) missing values Missing
KBA13_VORB_0 has 51281 (26.8%) missing values Missing
KBA13_VORB_1 has 51281 (26.8%) missing values Missing
KBA13_VORB_1_2 has 51281 (26.8%) missing values Missing
KBA13_VORB_2 has 51281 (26.8%) missing values Missing
KBA13_VORB_3 has 51281 (26.8%) missing values Missing
KBA13_VW has 51281 (26.8%) missing values Missing
KK_KUNDENTYP has 111937 (58.4%) missing values Missing
KKK has 54260 (28.3%) missing values Missing
KONSUMNAEHE has 46651 (24.3%) missing values Missing
KONSUMZELLE has 49927 (26.1%) missing values Missing
LP_FAMILIE_FEIN has 3213 (1.7%) missing values Missing
LP_FAMILIE_GROB has 3213 (1.7%) missing values Missing
LP_LEBENSPHASE_FEIN has 3213 (1.7%) missing values Missing
LP_LEBENSPHASE_GROB has 3213 (1.7%) missing values Missing
LP_STATUS_FEIN has 3213 (1.7%) missing values Missing
LP_STATUS_GROB has 3213 (1.7%) missing values Missing
MIN_GEBAEUDEJAHR has 49927 (26.1%) missing values Missing
MOBI_RASTER has 49927 (26.1%) missing values Missing
MOBI_REGIO has 55980 (29.2%) missing values Missing
ONLINE_AFFINITAET has 3213 (1.7%) missing values Missing
ORTSGR_KLS9 has 50476 (26.3%) missing values Missing
OST_WEST_KZ has 49927 (26.1%) missing values Missing
PLZ8_ANTG1 has 52764 (27.5%) missing values Missing
PLZ8_ANTG2 has 52764 (27.5%) missing values Missing
PLZ8_ANTG3 has 52764 (27.5%) missing values Missing
PLZ8_ANTG4 has 52764 (27.5%) missing values Missing
PLZ8_BAUMAX has 52764 (27.5%) missing values Missing
PLZ8_GBZ has 52764 (27.5%) missing values Missing
PLZ8_HHZ has 52764 (27.5%) missing values Missing
REGIOTYP has 54260 (28.3%) missing values Missing
RELAT_AB has 50476 (26.3%) missing values Missing
RETOURTYP_BK_S has 3213 (1.7%) missing values Missing
RT_KEIN_ANREIZ has 3213 (1.7%) missing values Missing
RT_SCHNAEPPCHEN has 3213 (1.7%) missing values Missing
RT_UEBERGROESSE has 44192 (23.1%) missing values Missing
SOHO_KZ has 46596 (24.3%) missing values Missing
STRUKTURTYP has 50476 (26.3%) missing values Missing
TITEL_KZ has 46596 (24.3%) missing values Missing
UMFELD_ALT has 50448 (26.3%) missing values Missing
UMFELD_JUNG has 50448 (26.3%) missing values Missing
UNGLEICHENN_FLAG has 46596 (24.3%) missing values Missing
VERDICHTUNGSRAUM has 50476 (26.3%) missing values Missing
VHA has 46596 (24.3%) missing values Missing
VHN has 54260 (28.3%) missing values Missing
VK_DHT4A has 47871 (25.0%) missing values Missing
VK_DISTANZ has 47871 (25.0%) missing values Missing
VK_ZG11 has 47871 (25.0%) missing values Missing
W_KEIT_KIND_HH has 53742 (28.0%) missing values Missing
WOHNDAUER_2008 has 46596 (24.3%) missing values Missing
WOHNLAGE has 49927 (26.1%) missing values Missing
ANZ_HH_TITEL is highly skewed (γ1 = 21.21510331) Skewed
LNR is uniformly distributed Uniform
LNR has unique values Unique
ALTER_HH has 22151 (11.6%) zeros Zeros
ALTERSKATEGORIE_FEIN has 11019 (5.7%) zeros Zeros
ANZ_HAUSHALTE_AKTIV has 2450 (1.3%) zeros Zeros
ANZ_HH_TITEL has 133454 (69.6%) zeros Zeros
ANZ_KINDER has 132284 (69.0%) zeros Zeros
ANZ_PERSONEN has 7146 (3.7%) zeros Zeros
D19_BANKEN_ANZ_12 has 180150 (94.0%) zeros Zeros
D19_BANKEN_ANZ_24 has 173701 (90.6%) zeros Zeros
D19_BANKEN_DIREKT has 166726 (87.0%) zeros Zeros
D19_BANKEN_GROSS has 175064 (91.3%) zeros Zeros
D19_BANKEN_LOKAL has 187347 (97.8%) zeros Zeros
D19_BANKEN_ONLINE_QUOTE_12 has 137161 (71.6%) zeros Zeros
D19_BANKEN_REST has 176243 (92.0%) zeros Zeros
D19_BEKLEIDUNG_GEH has 154242 (80.5%) zeros Zeros
D19_BEKLEIDUNG_REST has 137848 (71.9%) zeros Zeros
D19_BILDUNG has 155747 (81.3%) zeros Zeros
D19_BIO_OEKO has 174542 (91.1%) zeros Zeros
D19_BUCH_CD has 102937 (53.7%) zeros Zeros
D19_DIGIT_SERV has 183539 (95.8%) zeros Zeros
D19_DROGERIEARTIKEL has 160837 (83.9%) zeros Zeros
D19_ENERGIE has 172916 (90.2%) zeros Zeros
D19_FREIZEIT has 166363 (86.8%) zeros Zeros
D19_GARTEN has 179969 (93.9%) zeros Zeros
D19_GESAMT_ANZ_12 has 111999 (58.4%) zeros Zeros
D19_GESAMT_ANZ_24 has 91722 (47.9%) zeros Zeros
D19_GESAMT_ONLINE_QUOTE_12 has 86879 (45.3%) zeros Zeros
D19_HANDWERK has 143537 (74.9%) zeros Zeros
D19_HAUS_DEKO has 132811 (69.3%) zeros Zeros
D19_KINDERARTIKEL has 153651 (80.2%) zeros Zeros
D19_KOSMETIK has 139367 (72.7%) zeros Zeros
D19_LEBENSMITTEL has 170971 (89.2%) zeros Zeros
D19_LOTTO has 88281 (46.1%) zeros Zeros
D19_NAHRUNGSERGAENZUNG has 174094 (90.8%) zeros Zeros
D19_RATGEBER has 161270 (84.1%) zeros Zeros
D19_REISEN has 134825 (70.3%) zeros Zeros
D19_SAMMELARTIKEL has 145113 (75.7%) zeros Zeros
D19_SCHUHE has 163720 (85.4%) zeros Zeros
D19_SONSTIGE has 76573 (40.0%) zeros Zeros
D19_SOZIALES has 22750 (11.9%) zeros Zeros
D19_TECHNIK has 117416 (61.3%) zeros Zeros
D19_TELKO_ANZ_12 has 184467 (96.3%) zeros Zeros
D19_TELKO_ANZ_24 has 178411 (93.1%) zeros Zeros
D19_TELKO_MOBILE has 159544 (83.2%) zeros Zeros
D19_TELKO_REST has 168650 (88.0%) zeros Zeros
D19_TIERARTIKEL has 183788 (95.9%) zeros Zeros
D19_VERSAND_ANZ_12 has 122306 (63.8%) zeros Zeros
D19_VERSAND_ANZ_24 has 102484 (53.5%) zeros Zeros
D19_VERSAND_ONLINE_QUOTE_12 has 92458 (48.2%) zeros Zeros
D19_VERSAND_REST has 161199 (84.1%) zeros Zeros
D19_VERSI_ANZ_12 has 177236 (92.5%) zeros Zeros
D19_VERSI_ANZ_24 has 168832 (88.1%) zeros Zeros
D19_VERSICHERUNGEN has 144720 (75.5%) zeros Zeros
D19_VOLLSORTIMENT has 108259 (56.5%) zeros Zeros
D19_WEIN_FEINKOST has 166431 (86.8%) zeros Zeros
GEBURTSJAHR has 93024 (48.5%) zeros Zeros
KBA05_ALTER1 has 22932 (12.0%) zeros Zeros
KBA05_ALTER4 has 2435 (1.3%) zeros Zeros
KBA05_BAUMAX has 53555 (27.9%) zeros Zeros
KBA05_CCM4 has 32427 (16.9%) zeros Zeros
KBA05_DIESEL has 5076 (2.6%) zeros Zeros
KBA05_HERST1 has 5354 (2.8%) zeros Zeros
KBA05_HERST3 has 1918 (1.0%) zeros Zeros
KBA05_HERST4 has 3323 (1.7%) zeros Zeros
KBA05_HERST5 has 6536 (3.4%) zeros Zeros
KBA05_KW3 has 20709 (10.8%) zeros Zeros
KBA05_MOD1 has 32586 (17.0%) zeros Zeros
KBA05_MOD4 has 5107 (2.7%) zeros Zeros
KBA05_SEG10 has 9635 (5.0%) zeros Zeros
KBA05_SEG5 has 18467 (9.6%) zeros Zeros
KBA05_VORB2 has 11641 (6.1%) zeros Zeros
KBA05_ZUL3 has 5137 (2.7%) zeros Zeros
KBA05_ZUL4 has 8776 (4.6%) zeros Zeros
KBA13_BJ_2008 has 19625 (10.2%) zeros Zeros
KBA13_BJ_2009 has 16351 (8.5%) zeros Zeros
KBA13_CCM_0_1400 has 28511 (14.9%) zeros Zeros
KBA13_CCM_1000 has 19982 (10.4%) zeros Zeros
KBA13_CCM_1200 has 29378 (15.3%) zeros Zeros
KBA13_CCM_1800 has 24878 (13.0%) zeros Zeros
KBA13_CCM_2500 has 15780 (8.2%) zeros Zeros
KBA13_CCM_2501 has 14392 (7.5%) zeros Zeros
KBA13_CCM_3000 has 9117 (4.8%) zeros Zeros
KBA13_KMH_0_140 has 21541 (11.2%) zeros Zeros
KBA13_KMH_211 has 20007 (10.4%) zeros Zeros
KBA13_KMH_250 has 20125 (10.5%) zeros Zeros
KBA13_KW_110 has 19148 (10.0%) zeros Zeros
KBA13_KW_120 has 17157 (9.0%) zeros Zeros
KBA13_KW_121 has 15218 (7.9%) zeros Zeros
KBA13_KW_40 has 19498 (10.2%) zeros Zeros
KBA13_KW_50 has 29204 (15.2%) zeros Zeros
KBA13_KW_60 has 24799 (12.9%) zeros Zeros
KBA13_KW_70 has 27083 (14.1%) zeros Zeros
KBA13_KW_80 has 23396 (12.2%) zeros Zeros
KBA13_KW_90 has 23538 (12.3%) zeros Zeros
KBA13_SEG_OBERKLASSE has 14998 (7.8%) zeros Zeros
KBA13_SEG_SPORTWAGEN has 14430 (7.5%) zeros Zeros
KBA13_SEG_WOHNMOBILE has 15839 (8.3%) zeros Zeros
KBA13_VORB_3 has 31766 (16.6%) zeros Zeros
LP_FAMILIE_FEIN has 47369 (24.7%) zeros Zeros
LP_FAMILIE_GROB has 47369 (24.7%) zeros Zeros
LP_LEBENSPHASE_FEIN has 47840 (25.0%) zeros Zeros
LP_LEBENSPHASE_GROB has 47728 (24.9%) zeros Zeros
ONLINE_AFFINITAET has 4110 (2.1%) zeros Zeros
PRAEGENDE_JUGENDJAHRE has 48487 (25.3%) zeros Zeros
REGIOTYP has 5804 (3.0%) zeros Zeros
VERDICHTUNGSRAUM has 65984 (34.4%) zeros Zeros
VHA has 74250 (38.7%) zeros Zeros
W_KEIT_KIND_HH has 3195 (1.7%) zeros Zeros

Reproduction

Analysis started2021-12-24 10:25:40.539706
Analysis finished2021-12-24 10:27:42.739251
Duration2 minutes and 2.2 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

LNR
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct191652
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95826.5
Minimum1
Maximum191652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:42.949468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9583.55
Q147913.75
median95826.5
Q3143739.25
95-th percentile182069.45
Maximum191652
Range191651
Interquartile range (IQR)95825.5

Descriptive statistics

Standard deviation55325.31123
Coefficient of variation (CV)0.5773487629
Kurtosis-1.2
Mean95826.5
Median Absolute Deviation (MAD)47913
Skewness-4.929942205 × 10-19
Sum1.836534038 × 1010
Variance3060890063
MonotonicityNot monotonic
2021-12-24T13:27:43.142061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
1611241
 
< 0.1%
1467971
 
< 0.1%
1447481
 
< 0.1%
1345071
 
< 0.1%
1324581
 
< 0.1%
1386011
 
< 0.1%
1365521
 
< 0.1%
1590791
 
< 0.1%
1570301
 
< 0.1%
Other values (191642)191642
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1916521
< 0.1%
1916511
< 0.1%
1916501
< 0.1%
1916491
< 0.1%
1916481
< 0.1%
1916471
< 0.1%
1916461
< 0.1%
1916451
< 0.1%
1916441
< 0.1%
1916431
< 0.1%

AGER_TYP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
-1
92107 
2
45874 
1
40382 
3
 
8658
0
 
4631

Length

Max length2
Median length1
Mean length1.480595037
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row-1
3rd row-1
4th row1
5th row-1

Common Values

ValueCountFrequency (%)
-192107
48.1%
245874
23.9%
140382
21.1%
38658
 
4.5%
04631
 
2.4%

Length

2021-12-24T13:27:43.302926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:43.383383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1132489
69.1%
245874
 
23.9%
38658
 
4.5%
04631
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AKT_DAT_KL
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean1.747525094
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:43.494046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.966333812
Coefficient of variation (CV)1.12521063
Kurtosis6.537006582
Mean1.747525094
Median Absolute Deviation (MAD)0
Skewness2.767734543
Sum253489
Variance3.86646866
MonotonicityNot monotonic
2021-12-24T13:27:43.594589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1119535
62.4%
26214
 
3.2%
95981
 
3.1%
54096
 
2.1%
32516
 
1.3%
42311
 
1.2%
71649
 
0.9%
61575
 
0.8%
81179
 
0.6%
(Missing)46596
 
24.3%
ValueCountFrequency (%)
1119535
62.4%
26214
 
3.2%
32516
 
1.3%
42311
 
1.2%
54096
 
2.1%
61575
 
0.8%
71649
 
0.9%
81179
 
0.6%
95981
 
3.1%
ValueCountFrequency (%)
95981
 
3.1%
81179
 
0.6%
71649
 
0.9%
61575
 
0.8%
54096
 
2.1%
42311
 
1.2%
32516
 
1.3%
26214
 
3.2%
1119535
62.4%

ALTER_HH
Real number (ℝ≥0)

MISSING
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean11.35200888
Minimum0
Maximum21
Zeros22151
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:43.735286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median11
Q316
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.275026252
Coefficient of variation (CV)0.5527679126
Kurtosis-0.642090923
Mean11.35200888
Median Absolute Deviation (MAD)4
Skewness-0.406629953
Sum1646677
Variance39.37595446
MonotonicityNot monotonic
2021-12-24T13:27:43.986773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
022151
11.6%
1014488
 
7.6%
912976
 
6.8%
119009
 
4.7%
218503
 
4.4%
88111
 
4.2%
168011
 
4.2%
127779
 
4.1%
157737
 
4.0%
177332
 
3.8%
Other values (11)38959
20.3%
(Missing)46596
24.3%
ValueCountFrequency (%)
022151
11.6%
213
 
< 0.1%
315
 
< 0.1%
452
 
< 0.1%
5228
 
0.1%
61848
 
1.0%
75307
 
2.8%
88111
 
4.2%
912976
6.8%
1014488
7.6%
ValueCountFrequency (%)
218503
4.4%
206554
3.4%
196136
3.2%
186450
3.4%
177332
3.8%
168011
4.2%
157737
4.0%
146192
3.2%
136164
3.2%
127779
4.1%

ALTER_KIND1
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)0.1%
Missing179886
Missing (%)93.9%
Infinite0
Infinite (%)0.0%
Mean12.3372429
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:44.107465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median13
Q316
95-th percentile18
Maximum18
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.006049613
Coefficient of variation (CV)0.3247119024
Kurtosis-0.9258573891
Mean12.3372429
Median Absolute Deviation (MAD)3
Skewness-0.3197443551
Sum145160
Variance16.0484335
MonotonicityNot monotonic
2021-12-24T13:27:44.207979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
181158
 
0.6%
171076
 
0.6%
151030
 
0.5%
161027
 
0.5%
14949
 
0.5%
13882
 
0.5%
11813
 
0.4%
12791
 
0.4%
10779
 
0.4%
8766
 
0.4%
Other values (7)2495
 
1.3%
(Missing)179886
93.9%
ValueCountFrequency (%)
236
 
< 0.1%
3130
 
0.1%
4116
 
0.1%
5179
 
0.1%
6548
0.3%
7723
0.4%
8766
0.4%
9763
0.4%
10779
0.4%
11813
0.4%
ValueCountFrequency (%)
181158
0.6%
171076
0.6%
161027
0.5%
151030
0.5%
14949
0.5%
13882
0.5%
12791
0.4%
11813
0.4%
10779
0.4%
9763
0.4%

ALTER_KIND2
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)0.3%
Missing186552
Missing (%)97.3%
Infinite0
Infinite (%)0.0%
Mean13.67235294
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:44.338641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q111
median14
Q316
95-th percentile18
Maximum18
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.243334673
Coefficient of variation (CV)0.237218472
Kurtosis-0.5547774012
Mean13.67235294
Median Absolute Deviation (MAD)3
Skewness-0.5021796386
Sum69729
Variance10.5192198
MonotonicityNot monotonic
2021-12-24T13:27:44.449304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
18646
 
0.3%
16582
 
0.3%
17582
 
0.3%
15522
 
0.3%
13501
 
0.3%
14500
 
0.3%
12442
 
0.2%
11347
 
0.2%
10345
 
0.2%
9263
 
0.1%
Other values (7)370
 
0.2%
(Missing)186552
97.3%
ValueCountFrequency (%)
21
 
< 0.1%
34
 
< 0.1%
47
 
< 0.1%
523
 
< 0.1%
662
 
< 0.1%
799
 
0.1%
8174
0.1%
9263
0.1%
10345
0.2%
11347
0.2%
ValueCountFrequency (%)
18646
0.3%
17582
0.3%
16582
0.3%
15522
0.3%
14500
0.3%
13501
0.3%
12442
0.2%
11347
0.2%
10345
0.2%
9263
0.1%

ALTER_KIND3
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)1.1%
Missing190377
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean14.64705882
Minimum5
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:44.570096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q113
median15
Q317
95-th percentile18
Maximum18
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.753786671
Coefficient of variation (CV)0.1880095317
Kurtosis0.0957002455
Mean14.64705882
Median Absolute Deviation (MAD)2
Skewness-0.7774120716
Sum18675
Variance7.583341029
MonotonicityNot monotonic
2021-12-24T13:27:44.688643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
18209
 
0.1%
17186
 
0.1%
16179
 
0.1%
15165
 
0.1%
14135
 
0.1%
13135
 
0.1%
1285
 
< 0.1%
1168
 
< 0.1%
1050
 
< 0.1%
926
 
< 0.1%
Other values (4)37
 
< 0.1%
(Missing)190377
99.3%
ValueCountFrequency (%)
52
 
< 0.1%
66
 
< 0.1%
713
 
< 0.1%
816
 
< 0.1%
926
 
< 0.1%
1050
 
< 0.1%
1168
< 0.1%
1285
< 0.1%
13135
0.1%
14135
0.1%
ValueCountFrequency (%)
18209
0.1%
17186
0.1%
16179
0.1%
15165
0.1%
14135
0.1%
13135
0.1%
1285
< 0.1%
1168
 
< 0.1%
1050
 
< 0.1%
926
 
< 0.1%

ALTER_KIND4
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)4.2%
Missing191416
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean15.37711864
Minimum8
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:44.811301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11.75
Q114
median16
Q317
95-th percentile18
Maximum18
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.307652801
Coefficient of variation (CV)0.1500705597
Kurtosis0.02425654448
Mean15.37711864
Median Absolute Deviation (MAD)2
Skewness-0.7401942184
Sum3629
Variance5.32526145
MonotonicityNot monotonic
2021-12-24T13:27:44.911849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1853
 
< 0.1%
1746
 
< 0.1%
1534
 
< 0.1%
1326
 
< 0.1%
1425
 
< 0.1%
1624
 
< 0.1%
1216
 
< 0.1%
115
 
< 0.1%
104
 
< 0.1%
83
 
< 0.1%
(Missing)191416
99.9%
ValueCountFrequency (%)
83
 
< 0.1%
104
 
< 0.1%
115
 
< 0.1%
1216
 
< 0.1%
1326
< 0.1%
1425
< 0.1%
1534
< 0.1%
1624
< 0.1%
1746
< 0.1%
1853
< 0.1%
ValueCountFrequency (%)
1853
< 0.1%
1746
< 0.1%
1624
< 0.1%
1534
< 0.1%
1425
< 0.1%
1326
< 0.1%
1216
 
< 0.1%
115
 
< 0.1%
104
 
< 0.1%
83
 
< 0.1%

ALTERSKATEGORIE_FEIN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct25
Distinct (%)< 0.1%
Missing51842
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean10.33157857
Minimum0
Maximum25
Zeros11019
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:45.032632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median10
Q313
95-th percentile16
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.134827833
Coefficient of variation (CV)0.4002125914
Kurtosis1.204872188
Mean10.33157857
Median Absolute Deviation (MAD)2
Skewness-0.7083756147
Sum1444458
Variance17.09680121
MonotonicityNot monotonic
2021-12-24T13:27:45.151340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1020088
 
10.5%
919713
 
10.3%
1113508
 
7.0%
1212956
 
6.8%
811776
 
6.1%
1311629
 
6.1%
011019
 
5.7%
1410817
 
5.6%
158116
 
4.2%
77185
 
3.7%
Other values (15)13003
 
6.8%
(Missing)51842
27.1%
ValueCountFrequency (%)
011019
5.7%
212
 
< 0.1%
312
 
< 0.1%
469
 
< 0.1%
5292
 
0.2%
62375
 
1.2%
77185
 
3.7%
811776
6.1%
919713
10.3%
1020088
10.5%
ValueCountFrequency (%)
25102
 
0.1%
24132
 
0.1%
2324
 
< 0.1%
2271
 
< 0.1%
21221
 
0.1%
20454
 
0.2%
19774
 
0.4%
181510
 
0.8%
172478
1.3%
164477
2.3%

ANZ_HAUSHALTE_AKTIV
Real number (ℝ≥0)

MISSING
ZEROS

Distinct216
Distinct (%)0.2%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean4.965863468
Minimum0
Maximum523
Zeros2450
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:45.304151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum523
Range523
Interquartile range (IQR)3

Descriptive statistics

Standard deviation14.309694
Coefficient of variation (CV)2.881612452
Kurtosis210.7926573
Mean4.965863468
Median Absolute Deviation (MAD)0
Skewness12.06098307
Sum703787
Variance204.7673425
MonotonicityNot monotonic
2021-12-24T13:27:45.465038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172730
37.9%
222269
 
11.6%
37774
 
4.1%
44597
 
2.4%
53637
 
1.9%
63406
 
1.8%
73139
 
1.6%
82819
 
1.5%
02450
 
1.3%
92421
 
1.3%
Other values (206)16483
 
8.6%
(Missing)49927
26.1%
ValueCountFrequency (%)
02450
 
1.3%
172730
37.9%
222269
 
11.6%
37774
 
4.1%
44597
 
2.4%
53637
 
1.9%
63406
 
1.8%
73139
 
1.6%
82819
 
1.5%
92421
 
1.3%
ValueCountFrequency (%)
5231
 
< 0.1%
3951
 
< 0.1%
3795
< 0.1%
3671
 
< 0.1%
3662
 
< 0.1%
3481
 
< 0.1%
34410
< 0.1%
3311
 
< 0.1%
3218
< 0.1%
3113
 
< 0.1%

ANZ_HH_TITEL
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct20
Distinct (%)< 0.1%
Missing52110
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean0.06741339525
Minimum0
Maximum23
Zeros133454
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:45.605817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5455761376
Coefficient of variation (CV)8.092993025
Kurtosis604.3403899
Mean0.06741339525
Median Absolute Deviation (MAD)0
Skewness21.21510331
Sum9407
Variance0.2976533219
MonotonicityNot monotonic
2021-12-24T13:27:45.706291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0133454
69.6%
15186
 
2.7%
2459
 
0.2%
3112
 
0.1%
463
 
< 0.1%
652
 
< 0.1%
1339
 
< 0.1%
732
 
< 0.1%
826
 
< 0.1%
525
 
< 0.1%
Other values (10)94
 
< 0.1%
(Missing)52110
 
27.2%
ValueCountFrequency (%)
0133454
69.6%
15186
 
2.7%
2459
 
0.2%
3112
 
0.1%
463
 
< 0.1%
525
 
< 0.1%
652
 
< 0.1%
732
 
< 0.1%
826
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
236
 
< 0.1%
2010
 
< 0.1%
185
 
< 0.1%
1715
 
< 0.1%
155
 
< 0.1%
1412
 
< 0.1%
1339
< 0.1%
124
 
< 0.1%
1112
 
< 0.1%
108
 
< 0.1%

ANZ_KINDER
Real number (ℝ≥0)

MISSING
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean0.1364024928
Minimum0
Maximum8
Zeros132284
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:45.824883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4932493184
Coefficient of variation (CV)3.616131261
Kurtosis21.52904533
Mean0.1364024928
Median Absolute Deviation (MAD)0
Skewness4.316314013
Sum19786
Variance0.2432948901
MonotonicityNot monotonic
2021-12-24T13:27:45.944170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0132284
69.0%
17443
 
3.9%
23967
 
2.1%
31104
 
0.6%
4204
 
0.1%
546
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
(Missing)46596
 
24.3%
ValueCountFrequency (%)
0132284
69.0%
17443
 
3.9%
23967
 
2.1%
31104
 
0.6%
4204
 
0.1%
546
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
71
 
< 0.1%
66
 
< 0.1%
546
 
< 0.1%
4204
 
0.1%
31104
 
0.6%
23967
 
2.1%
17443
 
3.9%
0132284
69.0%

ANZ_PERSONEN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct18
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean2.267827598
Minimum0
Maximum21
Zeros7146
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:46.067900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.390620148
Coefficient of variation (CV)0.6131948256
Kurtosis1.819434865
Mean2.267827598
Median Absolute Deviation (MAD)1
Skewness0.9467205475
Sum328962
Variance1.933824396
MonotonicityNot monotonic
2021-12-24T13:27:46.178485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
243780
22.8%
140929
21.4%
327231
14.2%
416101
 
8.4%
07146
 
3.7%
56482
 
3.4%
62332
 
1.2%
7716
 
0.4%
8222
 
0.1%
965
 
< 0.1%
Other values (8)52
 
< 0.1%
(Missing)46596
24.3%
ValueCountFrequency (%)
07146
 
3.7%
140929
21.4%
243780
22.8%
327231
14.2%
416101
 
8.4%
56482
 
3.4%
62332
 
1.2%
7716
 
0.4%
8222
 
0.1%
965
 
< 0.1%
ValueCountFrequency (%)
212
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
143
 
< 0.1%
132
 
< 0.1%
129
 
< 0.1%
1112
 
< 0.1%
1022
 
< 0.1%
965
 
< 0.1%
8222
0.1%

ANZ_STATISTISCHE_HAUSHALTE
Real number (ℝ≥0)

MISSING

Distinct214
Distinct (%)0.2%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean4.701287705
Minimum0
Maximum375
Zeros1161
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:46.309161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile16
Maximum375
Range375
Interquartile range (IQR)2

Descriptive statistics

Standard deviation14.18408078
Coefficient of variation (CV)3.017062913
Kurtosis224.629693
Mean4.701287705
Median Absolute Deviation (MAD)0
Skewness12.67959573
Sum666290
Variance201.1881475
MonotonicityNot monotonic
2021-12-24T13:27:46.490330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177551
40.5%
220961
 
10.9%
37325
 
3.8%
44452
 
2.3%
53812
 
2.0%
63557
 
1.9%
73127
 
1.6%
82782
 
1.5%
92275
 
1.2%
101885
 
1.0%
Other values (204)13998
 
7.3%
(Missing)49927
26.1%
ValueCountFrequency (%)
01161
 
0.6%
177551
40.5%
220961
 
10.9%
37325
 
3.8%
44452
 
2.3%
53812
 
2.0%
63557
 
1.9%
73127
 
1.6%
82782
 
1.5%
92275
 
1.2%
ValueCountFrequency (%)
3755
 
< 0.1%
3711
 
< 0.1%
3651
 
< 0.1%
35410
< 0.1%
33919
< 0.1%
32211
< 0.1%
3171
 
< 0.1%
3142
 
< 0.1%
3091
 
< 0.1%
3045
 
< 0.1%

ANZ_TITEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Memory size1.5 MiB
0.0
142316 
1.0
 
2533
2.0
 
198
3.0
 
8
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0142316
74.3%
1.02533
 
1.3%
2.0198
 
0.1%
3.08
 
< 0.1%
5.01
 
< 0.1%
(Missing)46596
 
24.3%

Length

2021-12-24T13:27:46.651159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:46.739602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0142316
98.1%
1.02533
 
1.7%
2.0198
 
0.1%
3.08
 
< 0.1%
5.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ARBEIT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing50476
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean2.824849833
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:46.922553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.012414744
Coefficient of variation (CV)0.3583959518
Kurtosis-0.5611519002
Mean2.824849833
Median Absolute Deviation (MAD)1
Skewness-0.2080778722
Sum398801
Variance1.024983615
MonotonicityNot monotonic
2021-12-24T13:27:47.012979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
350905
26.6%
437595
19.6%
233334
17.4%
116941
 
8.8%
52378
 
1.2%
923
 
< 0.1%
(Missing)50476
26.3%
ValueCountFrequency (%)
116941
 
8.8%
233334
17.4%
350905
26.6%
437595
19.6%
52378
 
1.2%
923
 
< 0.1%
ValueCountFrequency (%)
923
 
< 0.1%
52378
 
1.2%
437595
19.6%
350905
26.6%
233334
17.4%
116941
 
8.8%

BALLRAUM
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing49959
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean4.301758026
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:47.123577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.114613777
Coefficient of variation (CV)0.4915696708
Kurtosis-1.425908566
Mean4.301758026
Median Absolute Deviation (MAD)2
Skewness-0.331649198
Sum609529
Variance4.471591424
MonotonicityNot monotonic
2021-12-24T13:27:47.234154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
648075
25.1%
121097
11.0%
220117
10.5%
717873
 
9.3%
313240
 
6.9%
411538
 
6.0%
59753
 
5.1%
(Missing)49959
26.1%
ValueCountFrequency (%)
121097
11.0%
220117
10.5%
313240
 
6.9%
411538
 
6.0%
59753
 
5.1%
648075
25.1%
717873
 
9.3%
ValueCountFrequency (%)
717873
 
9.3%
648075
25.1%
59753
 
5.1%
411538
 
6.0%
313240
 
6.9%
220117
10.5%
121097
11.0%

CAMEO_DEU_2015
Categorical

MISSING

Distinct45
Distinct (%)< 0.1%
Missing50428
Missing (%)26.3%
Memory size1.5 MiB
2D
11208 
6B
9634 
4C
 
9053
3D
 
8085
4A
 
7507
Other values (40)
95737 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1A
2nd row5D
3rd row4C
4th row7B
5th row5D

Common Values

ValueCountFrequency (%)
2D11208
 
5.8%
6B9634
 
5.0%
4C9053
 
4.7%
3D8085
 
4.2%
4A7507
 
3.9%
3C6628
 
3.5%
1D5880
 
3.1%
2C5076
 
2.6%
5D4546
 
2.4%
8A4252
 
2.2%
Other values (35)69355
36.2%
(Missing)50428
26.3%

Length

2021-12-24T13:27:47.364775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2d11208
 
7.9%
6b9634
 
6.8%
4c9053
 
6.4%
3d8085
 
5.7%
4a7507
 
5.3%
3c6628
 
4.7%
1d5880
 
4.2%
2c5076
 
3.6%
5d4546
 
3.2%
8a4252
 
3.0%
Other values (35)69355
49.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CAMEO_DEUG_2015
Categorical

MISSING

Distinct10
Distinct (%)< 0.1%
Missing50428
Missing (%)26.3%
Memory size1.5 MiB
2
23484 
4
22064 
6
18717 
3
18390 
1
16778 
Other values (5)
41791 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row4
4th row7
5th row5

Common Values

ValueCountFrequency (%)
223484
12.3%
422064
11.5%
618717
 
9.8%
318390
 
9.6%
116778
 
8.8%
813049
 
6.8%
511666
 
6.1%
710558
 
5.5%
96392
 
3.3%
X126
 
0.1%
(Missing)50428
26.3%

Length

2021-12-24T13:27:47.525774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:47.616202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
223484
16.6%
422064
15.6%
618717
13.3%
318390
13.0%
116778
11.9%
813049
9.2%
511666
8.3%
710558
7.5%
96392
 
4.5%
x126
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CAMEO_INTL_2015
Categorical

MISSING

Distinct22
Distinct (%)< 0.1%
Missing50428
Missing (%)26.3%
Memory size1.5 MiB
14
19647 
24
17805 
41
11320 
43
9634 
25
9372 
Other values (17)
73446 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row34
3rd row24
4th row41
5th row34

Common Values

ValueCountFrequency (%)
1419647
 
10.3%
2417805
 
9.3%
4111320
 
5.9%
439634
 
5.0%
259372
 
4.9%
159217
 
4.8%
518113
 
4.2%
137683
 
4.0%
227507
 
3.9%
235770
 
3.0%
Other values (12)35156
18.3%
(Missing)50428
26.3%

Length

2021-12-24T13:27:47.777090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1419647
13.9%
2417805
12.6%
4111320
 
8.0%
439634
 
6.8%
259372
 
6.6%
159217
 
6.5%
518113
 
5.7%
137683
 
5.4%
227507
 
5.3%
235770
 
4.1%
Other values (12)35156
24.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_GESAMTTYP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean3.677927605
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:47.887764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.813974805
Coefficient of variation (CV)0.4932056852
Kurtosis-1.453254602
Mean3.677927605
Median Absolute Deviation (MAD)2
Skewness0.01565975795
Sum693065
Variance3.290504592
MonotonicityNot monotonic
2021-12-24T13:27:47.988929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
651907
27.1%
242841
22.4%
426912
14.0%
324343
12.7%
124229
12.6%
518207
 
9.5%
(Missing)3213
 
1.7%
ValueCountFrequency (%)
124229
12.6%
242841
22.4%
324343
12.7%
426912
14.0%
518207
 
9.5%
651907
27.1%
ValueCountFrequency (%)
651907
27.1%
518207
 
9.5%
426912
14.0%
324343
12.7%
242841
22.4%
124229
12.6%

CJT_KATALOGNUTZER
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
5.0
107544 
4.0
27271 
3.0
22087 
1.0
20510 
2.0
11027 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row5.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0107544
56.1%
4.027271
 
14.2%
3.022087
 
11.5%
1.020510
 
10.7%
2.011027
 
5.8%
(Missing)3213
 
1.7%

Length

2021-12-24T13:27:48.119556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:48.210059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0107544
57.1%
4.027271
 
14.5%
3.022087
 
11.7%
1.020510
 
10.9%
2.011027
 
5.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
1.0
55916 
2.0
53362 
5.0
46069 
3.0
23080 
4.0
10012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.055916
29.2%
2.053362
27.8%
5.046069
24.0%
3.023080
12.0%
4.010012
 
5.2%
(Missing)3213
 
1.7%

Length

2021-12-24T13:27:48.330742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:48.431300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.055916
29.7%
2.053362
28.3%
5.046069
24.4%
3.023080
12.2%
4.010012
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
1.0
64716 
2.0
51794 
5.0
44294 
3.0
20079 
4.0
7556 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.064716
33.8%
2.051794
27.0%
5.044294
23.1%
3.020079
 
10.5%
4.07556
 
3.9%
(Missing)3213
 
1.7%

Length

2021-12-24T13:27:48.541892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:48.640398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.064716
34.3%
2.051794
27.5%
5.044294
23.5%
3.020079
 
10.7%
4.07556
 
4.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_3
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
5.0
133124 
4.0
30494 
3.0
15564 
2.0
 
7515
1.0
 
1742

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0133124
69.5%
4.030494
 
15.9%
3.015564
 
8.1%
2.07515
 
3.9%
1.01742
 
0.9%
(Missing)3213
 
1.7%

Length

2021-12-24T13:27:48.742905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:48.833372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0133124
70.6%
4.030494
 
16.2%
3.015564
 
8.3%
2.07515
 
4.0%
1.01742
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_4
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
5.0
127878 
4.0
32339 
3.0
 
11982
2.0
 
11086
1.0
 
5154

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0127878
66.7%
4.032339
 
16.9%
3.011982
 
6.3%
2.011086
 
5.8%
1.05154
 
2.7%
(Missing)3213
 
1.7%

Length

2021-12-24T13:27:48.964121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:49.044720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0127878
67.9%
4.032339
 
17.2%
3.011982
 
6.4%
2.011086
 
5.9%
1.05154
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_5
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
5.0
136309 
4.0
24855 
3.0
18855 
2.0
 
5786
1.0
 
2634

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0136309
71.1%
4.024855
 
13.0%
3.018855
 
9.8%
2.05786
 
3.0%
1.02634
 
1.4%
(Missing)3213
 
1.7%

Length

2021-12-24T13:27:49.165342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:49.243851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0136309
72.3%
4.024855
 
13.2%
3.018855
 
10.0%
2.05786
 
3.1%
1.02634
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_6
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
5.0
137643 
4.0
27282 
3.0
 
12791
2.0
 
8855
1.0
 
1868

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0137643
71.8%
4.027282
 
14.2%
3.012791
 
6.7%
2.08855
 
4.6%
1.01868
 
1.0%
(Missing)3213
 
1.7%

Length

2021-12-24T13:27:49.354358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:27:49.436782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0137643
73.0%
4.027282
 
14.5%
3.012791
 
6.8%
2.08855
 
4.7%
1.01868
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_BANKEN_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09104001002
Minimum0
Maximum6
Zeros180150
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:49.547440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4166841409
Coefficient of variation (CV)4.57693426
Kurtosis45.05976397
Mean0.09104001002
Median Absolute Deviation (MAD)0
Skewness6.025510973
Sum17448
Variance0.1736256733
MonotonicityNot monotonic
2021-12-24T13:27:49.657978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0180150
94.0%
17450
 
3.9%
22836
 
1.5%
3701
 
0.4%
4379
 
0.2%
5109
 
0.1%
627
 
< 0.1%
ValueCountFrequency (%)
0180150
94.0%
17450
 
3.9%
22836
 
1.5%
3701
 
0.4%
4379
 
0.2%
5109
 
0.1%
627
 
< 0.1%
ValueCountFrequency (%)
627
 
< 0.1%
5109
 
0.1%
4379
 
0.2%
3701
 
0.4%
22836
 
1.5%
17450
 
3.9%
0180150
94.0%

D19_BANKEN_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1595235114
Minimum0
Maximum6
Zeros173701
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:49.768491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5898238705
Coefficient of variation (CV)3.697410278
Kurtosis28.41577649
Mean0.1595235114
Median Absolute Deviation (MAD)0
Skewness4.868116602
Sum30573
Variance0.3478921983
MonotonicityNot monotonic
2021-12-24T13:27:49.867011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0173701
90.6%
110248
 
5.3%
24919
 
2.6%
31320
 
0.7%
4935
 
0.5%
5387
 
0.2%
6142
 
0.1%
ValueCountFrequency (%)
0173701
90.6%
110248
 
5.3%
24919
 
2.6%
31320
 
0.7%
4935
 
0.5%
5387
 
0.2%
6142
 
0.1%
ValueCountFrequency (%)
6142
 
0.1%
5387
 
0.2%
4935
 
0.5%
31320
 
0.7%
24919
 
2.6%
110248
 
5.3%
0173701
90.6%

D19_BANKEN_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.367598564
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:49.979623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.64326232
Coefficient of variation (CV)0.1754198057
Kurtosis10.03183108
Mean9.367598564
Median Absolute Deviation (MAD)0
Skewness-3.146670024
Sum1795319
Variance2.700311053
MonotonicityNot monotonic
2021-12-24T13:27:50.070095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10152762
79.7%
914819
 
7.7%
56458
 
3.4%
86120
 
3.2%
63257
 
1.7%
73192
 
1.7%
11988
 
1.0%
41172
 
0.6%
21136
 
0.6%
3748
 
0.4%
ValueCountFrequency (%)
11988
 
1.0%
21136
 
0.6%
3748
 
0.4%
41172
 
0.6%
56458
 
3.4%
63257
 
1.7%
73192
 
1.7%
86120
 
3.2%
914819
 
7.7%
10152762
79.7%
ValueCountFrequency (%)
10152762
79.7%
914819
 
7.7%
86120
 
3.2%
73192
 
1.7%
63257
 
1.7%
56458
 
3.4%
41172
 
0.6%
3748
 
0.4%
21136
 
0.6%
11988
 
1.0%

D19_BANKEN_DIREKT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6461659675
Minimum0
Maximum7
Zeros166726
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:50.180667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.771524943
Coefficient of variation (CV)2.741594315
Kurtosis4.988131207
Mean0.6461659675
Median Absolute Deviation (MAD)0
Skewness2.570975647
Sum123839
Variance3.138300623
MonotonicityNot monotonic
2021-12-24T13:27:50.299296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0166726
87.0%
611802
 
6.2%
34884
 
2.5%
52684
 
1.4%
72516
 
1.3%
21144
 
0.6%
41053
 
0.5%
1843
 
0.4%
ValueCountFrequency (%)
0166726
87.0%
1843
 
0.4%
21144
 
0.6%
34884
 
2.5%
41053
 
0.5%
52684
 
1.4%
611802
 
6.2%
72516
 
1.3%
ValueCountFrequency (%)
72516
 
1.3%
611802
 
6.2%
52684
 
1.4%
41053
 
0.5%
34884
 
2.5%
21144
 
0.6%
1843
 
0.4%
0166726
87.0%

D19_BANKEN_GROSS
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4244776992
Minimum0
Maximum6
Zeros175064
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:50.411882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.443738888
Coefficient of variation (CV)3.401212575
Kurtosis9.211679916
Mean0.4244776992
Median Absolute Deviation (MAD)0
Skewness3.287695646
Sum81352
Variance2.084381977
MonotonicityNot monotonic
2021-12-24T13:27:50.502490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0175064
91.3%
69097
 
4.7%
33040
 
1.6%
52488
 
1.3%
4875
 
0.5%
2622
 
0.3%
1466
 
0.2%
ValueCountFrequency (%)
0175064
91.3%
1466
 
0.2%
2622
 
0.3%
33040
 
1.6%
4875
 
0.5%
52488
 
1.3%
69097
 
4.7%
ValueCountFrequency (%)
69097
 
4.7%
52488
 
1.3%
4875
 
0.5%
33040
 
1.6%
2622
 
0.3%
1466
 
0.2%
0175064
91.3%

D19_BANKEN_LOKAL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1329701751
Minimum0
Maximum7
Zeros187347
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:50.623264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9079254017
Coefficient of variation (CV)6.828037948
Kurtosis47.40494858
Mean0.1329701751
Median Absolute Deviation (MAD)0
Skewness6.951105176
Sum25484
Variance0.824328535
MonotonicityNot monotonic
2021-12-24T13:27:50.872574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0187347
97.8%
72455
 
1.3%
3833
 
0.4%
6788
 
0.4%
5203
 
0.1%
222
 
< 0.1%
43
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
0187347
97.8%
11
 
< 0.1%
222
 
< 0.1%
3833
 
0.4%
43
 
< 0.1%
5203
 
0.1%
6788
 
0.4%
72455
 
1.3%
ValueCountFrequency (%)
72455
 
1.3%
6788
 
0.4%
5203
 
0.1%
43
 
< 0.1%
3833
 
0.4%
222
 
< 0.1%
11
 
< 0.1%
0187347
97.8%

D19_BANKEN_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.866471521
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:50.995064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7722532224
Coefficient of variation (CV)0.07827045573
Kurtosis48.23036322
Mean9.866471521
Median Absolute Deviation (MAD)0
Skewness-6.693493873
Sum1890929
Variance0.5963750395
MonotonicityNot monotonic
2021-12-24T13:27:51.085613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10184202
96.1%
52959
 
1.5%
82328
 
1.2%
91414
 
0.7%
2277
 
0.1%
6244
 
0.1%
187
 
< 0.1%
472
 
< 0.1%
741
 
< 0.1%
328
 
< 0.1%
ValueCountFrequency (%)
187
 
< 0.1%
2277
 
0.1%
328
 
< 0.1%
472
 
< 0.1%
52959
 
1.5%
6244
 
0.1%
741
 
< 0.1%
82328
 
1.2%
91414
 
0.7%
10184202
96.1%
ValueCountFrequency (%)
10184202
96.1%
91414
 
0.7%
82328
 
1.2%
741
 
< 0.1%
6244
 
0.1%
52959
 
1.5%
472
 
< 0.1%
328
 
< 0.1%
2277
 
0.1%
187
 
< 0.1%

D19_BANKEN_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.600645962
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:51.196153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.379280426
Coefficient of variation (CV)0.1436653774
Kurtosis19.49307895
Mean9.600645962
Median Absolute Deviation (MAD)0
Skewness-4.295434337
Sum1839983
Variance1.902414493
MonotonicityNot monotonic
2021-12-24T13:27:51.347969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10167585
87.4%
99622
 
5.0%
83137
 
1.6%
52912
 
1.5%
72473
 
1.3%
62041
 
1.1%
11738
 
0.9%
4864
 
0.5%
2712
 
0.4%
3568
 
0.3%
ValueCountFrequency (%)
11738
 
0.9%
2712
 
0.4%
3568
 
0.3%
4864
 
0.5%
52912
 
1.5%
62041
 
1.1%
72473
 
1.3%
83137
 
1.6%
99622
 
5.0%
10167585
87.4%
ValueCountFrequency (%)
10167585
87.4%
99622
 
5.0%
83137
 
1.6%
72473
 
1.3%
62041
 
1.1%
52912
 
1.5%
4864
 
0.5%
3568
 
0.3%
2712
 
0.4%
11738
 
0.9%

D19_BANKEN_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean0.4620193811
Minimum0
Maximum10
Zeros137161
Zeros (%)71.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:51.495330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.087401901
Coefficient of variation (CV)4.517996402
Kurtosis16.72476778
Mean0.4620193811
Median Absolute Deviation (MAD)0
Skewness4.320566308
Sum66510
Variance4.357246697
MonotonicityNot monotonic
2021-12-24T13:27:51.611192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0137161
71.6%
106463
 
3.4%
5140
 
0.1%
782
 
< 0.1%
344
 
< 0.1%
838
 
< 0.1%
914
 
< 0.1%
28
 
< 0.1%
64
 
< 0.1%
41
 
< 0.1%
(Missing)47697
 
24.9%
ValueCountFrequency (%)
0137161
71.6%
28
 
< 0.1%
344
 
< 0.1%
41
 
< 0.1%
5140
 
0.1%
64
 
< 0.1%
782
 
< 0.1%
838
 
< 0.1%
914
 
< 0.1%
106463
 
3.4%
ValueCountFrequency (%)
106463
 
3.4%
914
 
< 0.1%
838
 
< 0.1%
782
 
< 0.1%
64
 
< 0.1%
5140
 
0.1%
41
 
< 0.1%
344
 
< 0.1%
28
 
< 0.1%
0137161
71.6%

D19_BANKEN_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4444774905
Minimum0
Maximum7
Zeros176243
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:51.711299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.546226218
Coefficient of variation (CV)3.478750334
Kurtosis9.193731425
Mean0.4444774905
Median Absolute Deviation (MAD)0
Skewness3.304743405
Sum85185
Variance2.390815519
MonotonicityNot monotonic
2021-12-24T13:27:51.811538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0176243
92.0%
69620
 
5.0%
71829
 
1.0%
51643
 
0.9%
31559
 
0.8%
2400
 
0.2%
4204
 
0.1%
1154
 
0.1%
ValueCountFrequency (%)
0176243
92.0%
1154
 
0.1%
2400
 
0.2%
31559
 
0.8%
4204
 
0.1%
51643
 
0.9%
69620
 
5.0%
71829
 
1.0%
ValueCountFrequency (%)
71829
 
1.0%
69620
 
5.0%
51643
 
0.9%
4204
 
0.1%
31559
 
0.8%
2400
 
0.2%
1154
 
0.1%
0176243
92.0%

D19_BEKLEIDUNG_GEH
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9787270678
Minimum0
Maximum7
Zeros154242
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:51.942619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.097214522
Coefficient of variation (CV)2.142798121
Kurtosis1.64052231
Mean0.9787270678
Median Absolute Deviation (MAD)0
Skewness1.836276363
Sum187575
Variance4.39830875
MonotonicityNot monotonic
2021-12-24T13:27:52.042857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0154242
80.5%
618154
 
9.5%
37838
 
4.1%
55515
 
2.9%
72948
 
1.5%
21349
 
0.7%
4874
 
0.5%
1732
 
0.4%
ValueCountFrequency (%)
0154242
80.5%
1732
 
0.4%
21349
 
0.7%
37838
 
4.1%
4874
 
0.5%
55515
 
2.9%
618154
 
9.5%
72948
 
1.5%
ValueCountFrequency (%)
72948
 
1.5%
618154
 
9.5%
55515
 
2.9%
4874
 
0.5%
37838
 
4.1%
21349
 
0.7%
1732
 
0.4%
0154242
80.5%

D19_BEKLEIDUNG_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.527779517
Minimum0
Maximum7
Zeros137848
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:52.168616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.562741055
Coefficient of variation (CV)1.677428599
Kurtosis-0.4473246664
Mean1.527779517
Median Absolute Deviation (MAD)0
Skewness1.186852035
Sum292802
Variance6.567641713
MonotonicityNot monotonic
2021-12-24T13:27:52.259048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0137848
71.9%
631571
 
16.5%
77880
 
4.1%
36749
 
3.5%
54074
 
2.1%
21555
 
0.8%
11137
 
0.6%
4838
 
0.4%
ValueCountFrequency (%)
0137848
71.9%
11137
 
0.6%
21555
 
0.8%
36749
 
3.5%
4838
 
0.4%
54074
 
2.1%
631571
 
16.5%
77880
 
4.1%
ValueCountFrequency (%)
77880
 
4.1%
631571
 
16.5%
54074
 
2.1%
4838
 
0.4%
36749
 
3.5%
21555
 
0.8%
11137
 
0.6%
0137848
71.9%

D19_BILDUNG
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9985651076
Minimum0
Maximum7
Zeros155747
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:52.369632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.215235176
Coefficient of variation (CV)2.218418367
Kurtosis1.900404957
Mean0.9985651076
Median Absolute Deviation (MAD)0
Skewness1.920619252
Sum191377
Variance4.907266883
MonotonicityNot monotonic
2021-12-24T13:27:52.470427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0155747
81.3%
617221
 
9.0%
78661
 
4.5%
25393
 
2.8%
32301
 
1.2%
51400
 
0.7%
4602
 
0.3%
1327
 
0.2%
ValueCountFrequency (%)
0155747
81.3%
1327
 
0.2%
25393
 
2.8%
32301
 
1.2%
4602
 
0.3%
51400
 
0.7%
617221
 
9.0%
78661
 
4.5%
ValueCountFrequency (%)
78661
 
4.5%
617221
 
9.0%
51400
 
0.7%
4602
 
0.3%
32301
 
1.2%
25393
 
2.8%
1327
 
0.2%
0155747
81.3%

D19_BIO_OEKO
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5384916411
Minimum0
Maximum7
Zeros174542
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:52.628784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.74644807
Coefficient of variation (CV)3.243222247
Kurtosis7.213871093
Mean0.5384916411
Median Absolute Deviation (MAD)0
Skewness3.005302448
Sum103203
Variance3.050080861
MonotonicityNot monotonic
2021-12-24T13:27:52.760267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0174542
91.1%
69423
 
4.9%
75351
 
2.8%
31180
 
0.6%
51103
 
0.6%
425
 
< 0.1%
225
 
< 0.1%
13
 
< 0.1%
ValueCountFrequency (%)
0174542
91.1%
13
 
< 0.1%
225
 
< 0.1%
31180
 
0.6%
425
 
< 0.1%
51103
 
0.6%
69423
 
4.9%
75351
 
2.8%
ValueCountFrequency (%)
75351
 
2.8%
69423
 
4.9%
51103
 
0.6%
425
 
< 0.1%
31180
 
0.6%
225
 
< 0.1%
13
 
< 0.1%
0174542
91.1%

D19_BUCH_CD
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.443987018
Minimum0
Maximum7
Zeros102937
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:52.889623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.822639395
Coefficient of variation (CV)1.154932238
Kurtosis-1.738895896
Mean2.443987018
Median Absolute Deviation (MAD)0
Skewness0.3960911885
Sum468395
Variance7.967293156
MonotonicityNot monotonic
2021-12-24T13:27:53.007983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0102937
53.7%
663216
33.0%
38064
 
4.2%
54763
 
2.5%
14326
 
2.3%
73074
 
1.6%
22920
 
1.5%
42352
 
1.2%
ValueCountFrequency (%)
0102937
53.7%
14326
 
2.3%
22920
 
1.5%
38064
 
4.2%
42352
 
1.2%
54763
 
2.5%
663216
33.0%
73074
 
1.6%
ValueCountFrequency (%)
73074
 
1.6%
663216
33.0%
54763
 
2.5%
42352
 
1.2%
38064
 
4.2%
22920
 
1.5%
14326
 
2.3%
0102937
53.7%

D19_DIGIT_SERV
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2182653977
Minimum0
Maximum7
Zeros183539
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:53.123842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.084784513
Coefficient of variation (CV)4.970025137
Kurtosis24.14842848
Mean0.2182653977
Median Absolute Deviation (MAD)0
Skewness5.029803628
Sum41831
Variance1.17675744
MonotonicityNot monotonic
2021-12-24T13:27:53.230589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0183539
95.8%
64233
 
2.2%
31582
 
0.8%
7922
 
0.5%
5765
 
0.4%
2434
 
0.2%
4121
 
0.1%
156
 
< 0.1%
ValueCountFrequency (%)
0183539
95.8%
156
 
< 0.1%
2434
 
0.2%
31582
 
0.8%
4121
 
0.1%
5765
 
0.4%
64233
 
2.2%
7922
 
0.5%
ValueCountFrequency (%)
7922
 
0.5%
64233
 
2.2%
5765
 
0.4%
4121
 
0.1%
31582
 
0.8%
2434
 
0.2%
156
 
< 0.1%
0183539
95.8%

D19_DROGERIEARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.757998873
Minimum0
Maximum7
Zeros160837
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:53.346453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.85788388
Coefficient of variation (CV)2.451037787
Kurtosis3.551151404
Mean0.757998873
Median Absolute Deviation (MAD)0
Skewness2.266085638
Sum145272
Variance3.451732512
MonotonicityNot monotonic
2021-12-24T13:27:53.446691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0160837
83.9%
611927
 
6.2%
36313
 
3.3%
54431
 
2.3%
72678
 
1.4%
42130
 
1.1%
22014
 
1.1%
11322
 
0.7%
ValueCountFrequency (%)
0160837
83.9%
11322
 
0.7%
22014
 
1.1%
36313
 
3.3%
42130
 
1.1%
54431
 
2.3%
611927
 
6.2%
72678
 
1.4%
ValueCountFrequency (%)
72678
 
1.4%
611927
 
6.2%
54431
 
2.3%
42130
 
1.1%
36313
 
3.3%
22014
 
1.1%
11322
 
0.7%
0160837
83.9%

D19_ENERGIE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4742658569
Minimum0
Maximum7
Zeros172916
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:53.562188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.52271566
Coefficient of variation (CV)3.210679492
Kurtosis8.562474788
Mean0.4742658569
Median Absolute Deviation (MAD)0
Skewness3.152478177
Sum90894
Variance2.318662982
MonotonicityNot monotonic
2021-12-24T13:27:53.662427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0172916
90.2%
66859
 
3.6%
35364
 
2.8%
53125
 
1.6%
72166
 
1.1%
2697
 
0.4%
4314
 
0.2%
1211
 
0.1%
ValueCountFrequency (%)
0172916
90.2%
1211
 
0.1%
2697
 
0.4%
35364
 
2.8%
4314
 
0.2%
53125
 
1.6%
66859
 
3.6%
72166
 
1.1%
ValueCountFrequency (%)
72166
 
1.1%
66859
 
3.6%
53125
 
1.6%
4314
 
0.2%
35364
 
2.8%
2697
 
0.4%
1211
 
0.1%
0172916
90.2%

D19_FREIZEIT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6884144178
Minimum0
Maximum7
Zeros166363
Zeros (%)86.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:53.783708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.838986968
Coefficient of variation (CV)2.67133709
Kurtosis4.252870867
Mean0.6884144178
Median Absolute Deviation (MAD)0
Skewness2.441847599
Sum131936
Variance3.381873068
MonotonicityNot monotonic
2021-12-24T13:27:53.904364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0166363
86.8%
613275
 
6.9%
34361
 
2.3%
53415
 
1.8%
72448
 
1.3%
4849
 
0.4%
2655
 
0.3%
1286
 
0.1%
ValueCountFrequency (%)
0166363
86.8%
1286
 
0.1%
2655
 
0.3%
34361
 
2.3%
4849
 
0.4%
53415
 
1.8%
613275
 
6.9%
72448
 
1.3%
ValueCountFrequency (%)
72448
 
1.3%
613275
 
6.9%
53415
 
1.8%
4849
 
0.4%
34361
 
2.3%
2655
 
0.3%
1286
 
0.1%
0166363
86.8%

D19_GARTEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3445254941
Minimum0
Maximum7
Zeros179969
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:54.014936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.383708292
Coefficient of variation (CV)4.016272572
Kurtosis13.52401246
Mean0.3445254941
Median Absolute Deviation (MAD)0
Skewness3.891901155
Sum66029
Variance1.914648638
MonotonicityNot monotonic
2021-12-24T13:27:54.113469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0179969
93.9%
66330
 
3.3%
72176
 
1.1%
51655
 
0.9%
31319
 
0.7%
499
 
0.1%
285
 
< 0.1%
119
 
< 0.1%
ValueCountFrequency (%)
0179969
93.9%
119
 
< 0.1%
285
 
< 0.1%
31319
 
0.7%
499
 
0.1%
51655
 
0.9%
66330
 
3.3%
72176
 
1.1%
ValueCountFrequency (%)
72176
 
1.1%
66330
 
3.3%
51655
 
0.9%
499
 
0.1%
31319
 
0.7%
285
 
< 0.1%
119
 
< 0.1%
0179969
93.9%

D19_GESAMT_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9376787093
Minimum0
Maximum6
Zeros111999
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:54.226062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.376459195
Coefficient of variation (CV)1.467943317
Kurtosis1.195739304
Mean0.9376787093
Median Absolute Deviation (MAD)0
Skewness1.441028864
Sum179708
Variance1.894639916
MonotonicityNot monotonic
2021-12-24T13:27:54.326652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0111999
58.4%
127777
 
14.5%
225069
 
13.1%
311048
 
5.8%
410882
 
5.7%
54141
 
2.2%
6736
 
0.4%
ValueCountFrequency (%)
0111999
58.4%
127777
 
14.5%
225069
 
13.1%
311048
 
5.8%
410882
 
5.7%
54141
 
2.2%
6736
 
0.4%
ValueCountFrequency (%)
6736
 
0.4%
54141
 
2.2%
410882
 
5.7%
311048
 
5.8%
225069
 
13.1%
127777
 
14.5%
0111999
58.4%

D19_GESAMT_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.438581387
Minimum0
Maximum6
Zeros91722
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:54.435187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.742773839
Coefficient of variation (CV)1.211453071
Kurtosis-0.2757674674
Mean1.438581387
Median Absolute Deviation (MAD)1
Skewness0.9619164175
Sum275707
Variance3.037260654
MonotonicityNot monotonic
2021-12-24T13:27:54.527803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
091722
47.9%
227736
 
14.5%
124260
 
12.7%
417771
 
9.3%
315045
 
7.9%
510952
 
5.7%
64166
 
2.2%
ValueCountFrequency (%)
091722
47.9%
124260
 
12.7%
227736
 
14.5%
315045
 
7.9%
417771
 
9.3%
510952
 
5.7%
64166
 
2.2%
ValueCountFrequency (%)
64166
 
2.2%
510952
 
5.7%
417771
 
9.3%
315045
 
7.9%
227736
 
14.5%
124260
 
12.7%
091722
47.9%

D19_GESAMT_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.52949617
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:54.618273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.246551658
Coefficient of variation (CV)0.4972131958
Kurtosis-1.30427266
Mean6.52949617
Median Absolute Deviation (MAD)3
Skewness-0.3805784073
Sum1251391
Variance10.54009767
MonotonicityNot monotonic
2021-12-24T13:27:54.718880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1060536
31.6%
525477
13.3%
119626
 
10.2%
919071
 
10.0%
214981
 
7.8%
812115
 
6.3%
611313
 
5.9%
411166
 
5.8%
78964
 
4.7%
38403
 
4.4%
ValueCountFrequency (%)
119626
 
10.2%
214981
 
7.8%
38403
 
4.4%
411166
 
5.8%
525477
13.3%
611313
 
5.9%
78964
 
4.7%
812115
 
6.3%
919071
 
10.0%
1060536
31.6%
ValueCountFrequency (%)
1060536
31.6%
919071
 
10.0%
812115
 
6.3%
78964
 
4.7%
611313
 
5.9%
525477
13.3%
411166
 
5.8%
38403
 
4.4%
214981
 
7.8%
119626
 
10.2%

D19_GESAMT_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.412435039
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:54.829512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.18512237
Coefficient of variation (CV)0.2597490928
Kurtosis1.701849686
Mean8.412435039
Median Absolute Deviation (MAD)1
Skewness-1.550164165
Sum1612260
Variance4.774759771
MonotonicityNot monotonic
2021-12-24T13:27:54.938062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1089296
46.6%
936977
19.3%
820973
 
10.9%
515032
 
7.8%
79313
 
4.9%
67942
 
4.1%
23673
 
1.9%
43404
 
1.8%
12684
 
1.4%
32358
 
1.2%
ValueCountFrequency (%)
12684
 
1.4%
23673
 
1.9%
32358
 
1.2%
43404
 
1.8%
515032
 
7.8%
67942
 
4.1%
79313
 
4.9%
820973
 
10.9%
936977
19.3%
1089296
46.6%
ValueCountFrequency (%)
1089296
46.6%
936977
19.3%
820973
 
10.9%
79313
 
4.9%
67942
 
4.1%
515032
 
7.8%
43404
 
1.8%
32358
 
1.2%
23673
 
1.9%
12684
 
1.4%

D19_GESAMT_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.445714107
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:55.048682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.117772196
Coefficient of variation (CV)0.4187338046
Kurtosis-0.7243027577
Mean7.445714107
Median Absolute Deviation (MAD)1
Skewness-0.8570109421
Sum1426986
Variance9.720503465
MonotonicityNot monotonic
2021-12-24T13:27:55.131109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1089840
46.9%
518652
 
9.7%
918203
 
9.5%
114207
 
7.4%
29627
 
5.0%
89435
 
4.9%
69313
 
4.9%
48539
 
4.5%
77754
 
4.0%
36082
 
3.2%
ValueCountFrequency (%)
114207
 
7.4%
29627
 
5.0%
36082
 
3.2%
48539
 
4.5%
518652
 
9.7%
69313
 
4.9%
77754
 
4.0%
89435
 
4.9%
918203
 
9.5%
1089840
46.9%
ValueCountFrequency (%)
1089840
46.9%
918203
 
9.5%
89435
 
4.9%
77754
 
4.0%
69313
 
4.9%
518652
 
9.7%
48539
 
4.5%
36082
 
3.2%
29627
 
5.0%
114207
 
7.4%

D19_GESAMT_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean3.522878677
Minimum0
Maximum10
Zeros86879
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:55.231590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.561253201
Coefficient of variation (CV)1.294751713
Kurtosis-1.547743994
Mean3.522878677
Median Absolute Deviation (MAD)0
Skewness0.6049669116
Sum507136
Variance20.80503077
MonotonicityNot monotonic
2021-12-24T13:27:55.322107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
086879
45.3%
1042191
22.0%
54010
 
2.1%
82677
 
1.4%
72392
 
1.2%
31767
 
0.9%
91502
 
0.8%
2701
 
0.4%
6666
 
0.3%
1627
 
0.3%
(Missing)47697
24.9%
ValueCountFrequency (%)
086879
45.3%
1627
 
0.3%
2701
 
0.4%
31767
 
0.9%
4543
 
0.3%
54010
 
2.1%
6666
 
0.3%
72392
 
1.2%
82677
 
1.4%
91502
 
0.8%
ValueCountFrequency (%)
1042191
22.0%
91502
 
0.8%
82677
 
1.4%
72392
 
1.2%
6666
 
0.3%
54010
 
2.1%
4543
 
0.3%
31767
 
0.9%
2701
 
0.4%
1627
 
0.3%

D19_HANDWERK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.535256611
Minimum0
Maximum7
Zeros143537
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:55.422601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.668879944
Coefficient of variation (CV)1.73839339
Kurtosis-0.5466882006
Mean1.535256611
Median Absolute Deviation (MAD)0
Skewness1.186063581
Sum294235
Variance7.122920157
MonotonicityNot monotonic
2021-12-24T13:27:55.523037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0143537
74.9%
637125
 
19.4%
79153
 
4.8%
5954
 
0.5%
3821
 
0.4%
431
 
< 0.1%
226
 
< 0.1%
15
 
< 0.1%
ValueCountFrequency (%)
0143537
74.9%
15
 
< 0.1%
226
 
< 0.1%
3821
 
0.4%
431
 
< 0.1%
5954
 
0.5%
637125
 
19.4%
79153
 
4.8%
ValueCountFrequency (%)
79153
 
4.8%
637125
 
19.4%
5954
 
0.5%
431
 
< 0.1%
3821
 
0.4%
226
 
< 0.1%
15
 
< 0.1%
0143537
74.9%

D19_HAUS_DEKO
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.53703066
Minimum0
Maximum7
Zeros132811
Zeros (%)69.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:55.862604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.459250091
Coefficient of variation (CV)1.600000674
Kurtosis-0.5493058082
Mean1.53703066
Median Absolute Deviation (MAD)0
Skewness1.121730654
Sum294575
Variance6.047911012
MonotonicityNot monotonic
2021-12-24T13:27:55.953127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0132811
69.3%
632600
 
17.0%
311291
 
5.9%
56851
 
3.6%
72719
 
1.4%
22708
 
1.4%
11430
 
0.7%
41242
 
0.6%
ValueCountFrequency (%)
0132811
69.3%
11430
 
0.7%
22708
 
1.4%
311291
 
5.9%
41242
 
0.6%
56851
 
3.6%
632600
 
17.0%
72719
 
1.4%
ValueCountFrequency (%)
72719
 
1.4%
632600
 
17.0%
56851
 
3.6%
41242
 
0.6%
311291
 
5.9%
22708
 
1.4%
11430
 
0.7%
0132811
69.3%

D19_KINDERARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.083515956
Minimum0
Maximum7
Zeros153651
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:56.053707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.277333103
Coefficient of variation (CV)2.101799323
Kurtosis1.237898453
Mean1.083515956
Median Absolute Deviation (MAD)0
Skewness1.745927238
Sum207658
Variance5.18624606
MonotonicityNot monotonic
2021-12-24T13:27:56.162902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0153651
80.2%
619662
 
10.3%
77585
 
4.0%
34723
 
2.5%
53125
 
1.6%
21770
 
0.9%
4707
 
0.4%
1429
 
0.2%
ValueCountFrequency (%)
0153651
80.2%
1429
 
0.2%
21770
 
0.9%
34723
 
2.5%
4707
 
0.4%
53125
 
1.6%
619662
 
10.3%
77585
 
4.0%
ValueCountFrequency (%)
77585
 
4.0%
619662
 
10.3%
53125
 
1.6%
4707
 
0.4%
34723
 
2.5%
21770
 
0.9%
1429
 
0.2%
0153651
80.2%

D19_KONSUMTYP
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean3.027654475
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:56.326632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.206506896
Coefficient of variation (CV)0.7287842501
Kurtosis2.031475141
Mean3.027654475
Median Absolute Deviation (MAD)1
Skewness1.646762171
Sum435846
Variance4.868672684
MonotonicityNot monotonic
2021-12-24T13:27:56.426363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
352323
27.3%
135794
18.7%
229600
15.4%
912516
 
6.5%
67424
 
3.9%
44795
 
2.5%
51503
 
0.8%
(Missing)47697
24.9%
ValueCountFrequency (%)
135794
18.7%
229600
15.4%
352323
27.3%
44795
 
2.5%
51503
 
0.8%
67424
 
3.9%
912516
 
6.5%
ValueCountFrequency (%)
912516
 
6.5%
67424
 
3.9%
51503
 
0.8%
44795
 
2.5%
352323
27.3%
229600
15.4%
135794
18.7%

D19_KONSUMTYP_MAX
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.224469351
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:56.525781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.198298122
Coefficient of variation (CV)0.7570887267
Kurtosis-1.400897755
Mean4.224469351
Median Absolute Deviation (MAD)1
Skewness0.6773018872
Sum809628
Variance10.22911088
MonotonicityNot monotonic
2021-12-24T13:27:56.652277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
291758
47.9%
947697
24.9%
120075
 
10.5%
813911
 
7.3%
410843
 
5.7%
37368
 
3.8%
ValueCountFrequency (%)
120075
 
10.5%
291758
47.9%
37368
 
3.8%
410843
 
5.7%
813911
 
7.3%
947697
24.9%
ValueCountFrequency (%)
947697
24.9%
813911
 
7.3%
410843
 
5.7%
37368
 
3.8%
291758
47.9%
120075
 
10.5%

D19_KOSMETIK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.756110033
Minimum0
Maximum7
Zeros139367
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:56.815839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.883393243
Coefficient of variation (CV)1.641920602
Kurtosis-0.8435883915
Mean1.756110033
Median Absolute Deviation (MAD)0
Skewness1.054458807
Sum336562
Variance8.313956594
MonotonicityNot monotonic
2021-12-24T13:27:56.939509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0139367
72.7%
627497
 
14.3%
724193
 
12.6%
3268
 
0.1%
5234
 
0.1%
248
 
< 0.1%
438
 
< 0.1%
17
 
< 0.1%
ValueCountFrequency (%)
0139367
72.7%
17
 
< 0.1%
248
 
< 0.1%
3268
 
0.1%
438
 
< 0.1%
5234
 
0.1%
627497
 
14.3%
724193
 
12.6%
ValueCountFrequency (%)
724193
 
12.6%
627497
 
14.3%
5234
 
0.1%
438
 
< 0.1%
3268
 
0.1%
248
 
< 0.1%
17
 
< 0.1%
0139367
72.7%

D19_LEBENSMITTEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.577635506
Minimum0
Maximum7
Zeros170971
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:57.057193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.723675155
Coefficient of variation (CV)2.984018706
Kurtosis6.156141266
Mean0.577635506
Median Absolute Deviation (MAD)0
Skewness2.798068542
Sum110705
Variance2.971056041
MonotonicityNot monotonic
2021-12-24T13:27:57.169889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0170971
89.2%
610808
 
5.6%
33931
 
2.1%
72978
 
1.6%
52361
 
1.2%
2357
 
0.2%
4151
 
0.1%
195
 
< 0.1%
ValueCountFrequency (%)
0170971
89.2%
195
 
< 0.1%
2357
 
0.2%
33931
 
2.1%
4151
 
0.1%
52361
 
1.2%
610808
 
5.6%
72978
 
1.6%
ValueCountFrequency (%)
72978
 
1.6%
610808
 
5.6%
52361
 
1.2%
4151
 
0.1%
33931
 
2.1%
2357
 
0.2%
195
 
< 0.1%
0170971
89.2%

D19_LETZTER_KAUF_BRANCHE
Categorical

MISSING

Distinct35
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Memory size1.5 MiB
D19_UNBEKANNT
31910 
D19_SONSTIGE
14540 
D19_VERSICHERUNGEN
10534 
D19_BUCH_CD
10038 
D19_VOLLSORTIMENT
8647 
Other values (30)
68286 

Length

Max length22
Median length13
Mean length14.31579313
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD19_UNBEKANNT
2nd rowD19_BANKEN_GROSS
3rd rowD19_UNBEKANNT
4th rowD19_NAHRUNGSERGAENZUNG
5th rowD19_SCHUHE

Common Values

ValueCountFrequency (%)
D19_UNBEKANNT31910
16.6%
D19_SONSTIGE14540
 
7.6%
D19_VERSICHERUNGEN10534
 
5.5%
D19_BUCH_CD10038
 
5.2%
D19_VOLLSORTIMENT8647
 
4.5%
D19_HAUS_DEKO8129
 
4.2%
D19_SCHUHE6317
 
3.3%
D19_BEKLEIDUNG_GEH5975
 
3.1%
D19_DROGERIEARTIKEL5528
 
2.9%
D19_ENERGIE4454
 
2.3%
Other values (25)37883
19.8%
(Missing)47697
24.9%

Length

2021-12-24T13:27:57.325611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d19_unbekannt31910
22.2%
d19_sonstige14540
 
10.1%
d19_versicherungen10534
 
7.3%
d19_buch_cd10038
 
7.0%
d19_vollsortiment8647
 
6.0%
d19_haus_deko8129
 
5.6%
d19_schuhe6317
 
4.4%
d19_bekleidung_geh5975
 
4.2%
d19_drogerieartikel5528
 
3.8%
d19_energie4454
 
3.1%
Other values (25)37883
26.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_LOTTO
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean2.633732764
Minimum0
Maximum7
Zeros88281
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:57.457172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.332828243
Coefficient of variation (CV)1.265439033
Kurtosis-1.741519834
Mean2.633732764
Median Absolute Deviation (MAD)0
Skewness0.4884701068
Sum379139
Variance11.1077441
MonotonicityNot monotonic
2021-12-24T13:27:57.560895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
088281
46.1%
747084
24.6%
67609
 
4.0%
5481
 
0.3%
3468
 
0.2%
217
 
< 0.1%
413
 
< 0.1%
12
 
< 0.1%
(Missing)47697
24.9%
ValueCountFrequency (%)
088281
46.1%
12
 
< 0.1%
217
 
< 0.1%
3468
 
0.2%
413
 
< 0.1%
5481
 
0.3%
67609
 
4.0%
747084
24.6%
ValueCountFrequency (%)
747084
24.6%
67609
 
4.0%
5481
 
0.3%
413
 
< 0.1%
3468
 
0.2%
217
 
< 0.1%
12
 
< 0.1%
088281
46.1%

D19_NAHRUNGSERGAENZUNG
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5059169745
Minimum0
Maximum7
Zeros174094
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:57.662620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.651735897
Coefficient of variation (CV)3.264835893
Kurtosis7.966241302
Mean0.5059169745
Median Absolute Deviation (MAD)0
Skewness3.102989701
Sum96960
Variance2.728231474
MonotonicityNot monotonic
2021-12-24T13:27:57.759332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0174094
90.8%
68225
 
4.3%
74039
 
2.1%
32584
 
1.3%
52035
 
1.1%
2285
 
0.1%
1240
 
0.1%
4150
 
0.1%
ValueCountFrequency (%)
0174094
90.8%
1240
 
0.1%
2285
 
0.1%
32584
 
1.3%
4150
 
0.1%
52035
 
1.1%
68225
 
4.3%
74039
 
2.1%
ValueCountFrequency (%)
74039
 
2.1%
68225
 
4.3%
52035
 
1.1%
4150
 
0.1%
32584
 
1.3%
2285
 
0.1%
1240
 
0.1%
0174094
90.8%

D19_RATGEBER
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.799756851
Minimum0
Maximum7
Zeros161270
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:57.887983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.959001155
Coefficient of variation (CV)2.449495935
Kurtosis3.207414088
Mean0.799756851
Median Absolute Deviation (MAD)0
Skewness2.218221165
Sum153275
Variance3.837685526
MonotonicityNot monotonic
2021-12-24T13:27:58.002753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0161270
84.1%
615893
 
8.3%
33856
 
2.0%
23643
 
1.9%
73308
 
1.7%
52439
 
1.3%
4823
 
0.4%
1420
 
0.2%
ValueCountFrequency (%)
0161270
84.1%
1420
 
0.2%
23643
 
1.9%
33856
 
2.0%
4823
 
0.4%
52439
 
1.3%
615893
 
8.3%
73308
 
1.7%
ValueCountFrequency (%)
73308
 
1.7%
615893
 
8.3%
52439
 
1.3%
4823
 
0.4%
33856
 
2.0%
23643
 
1.9%
1420
 
0.2%
0161270
84.1%

D19_REISEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.775556738
Minimum0
Maximum7
Zeros134825
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:58.121433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.80323014
Coefficient of variation (CV)1.57878939
Kurtosis-0.9166628182
Mean1.775556738
Median Absolute Deviation (MAD)0
Skewness1.001647035
Sum340289
Variance7.858099217
MonotonicityNot monotonic
2021-12-24T13:27:58.217175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0134825
70.3%
634244
 
17.9%
716397
 
8.6%
22078
 
1.1%
31982
 
1.0%
51653
 
0.9%
4402
 
0.2%
171
 
< 0.1%
ValueCountFrequency (%)
0134825
70.3%
171
 
< 0.1%
22078
 
1.1%
31982
 
1.0%
4402
 
0.2%
51653
 
0.9%
634244
 
17.9%
716397
 
8.6%
ValueCountFrequency (%)
716397
 
8.6%
634244
 
17.9%
51653
 
0.9%
4402
 
0.2%
31982
 
1.0%
22078
 
1.1%
171
 
< 0.1%
0134825
70.3%

D19_SAMMELARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.432179158
Minimum0
Maximum7
Zeros145113
Zeros (%)75.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:58.328876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.551136246
Coefficient of variation (CV)1.781296866
Kurtosis-0.4070965951
Mean1.432179158
Median Absolute Deviation (MAD)0
Skewness1.245248736
Sum274480
Variance6.508296146
MonotonicityNot monotonic
2021-12-24T13:27:58.427430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0145113
75.7%
639605
 
20.7%
72958
 
1.5%
52069
 
1.1%
31607
 
0.8%
4197
 
0.1%
287
 
< 0.1%
116
 
< 0.1%
ValueCountFrequency (%)
0145113
75.7%
116
 
< 0.1%
287
 
< 0.1%
31607
 
0.8%
4197
 
0.1%
52069
 
1.1%
639605
 
20.7%
72958
 
1.5%
ValueCountFrequency (%)
72958
 
1.5%
639605
 
20.7%
52069
 
1.1%
4197
 
0.1%
31607
 
0.8%
287
 
< 0.1%
116
 
< 0.1%
0145113
75.7%

D19_SCHUHE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.598788429
Minimum0
Maximum7
Zeros163720
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:58.527264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.584453772
Coefficient of variation (CV)2.646099515
Kurtosis5.714806054
Mean0.598788429
Median Absolute Deviation (MAD)0
Skewness2.629301269
Sum114759
Variance2.510493755
MonotonicityNot monotonic
2021-12-24T13:27:58.647387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0163720
85.4%
39856
 
5.1%
67042
 
3.7%
54485
 
2.3%
23255
 
1.7%
71425
 
0.7%
11149
 
0.6%
4720
 
0.4%
ValueCountFrequency (%)
0163720
85.4%
11149
 
0.6%
23255
 
1.7%
39856
 
5.1%
4720
 
0.4%
54485
 
2.3%
67042
 
3.7%
71425
 
0.7%
ValueCountFrequency (%)
71425
 
0.7%
67042
 
3.7%
54485
 
2.3%
4720
 
0.4%
39856
 
5.1%
23255
 
1.7%
11149
 
0.6%
0163720
85.4%

D19_SONSTIGE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.26943627
Minimum0
Maximum7
Zeros76573
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:58.766824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.88042826
Coefficient of variation (CV)0.8810167938
Kurtosis-1.807866063
Mean3.26943627
Median Absolute Deviation (MAD)3
Skewness-0.09844980588
Sum626594
Variance8.296866961
MonotonicityNot monotonic
2021-12-24T13:27:58.872541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
076573
40.0%
667813
35.4%
715835
 
8.3%
314059
 
7.3%
59521
 
5.0%
23534
 
1.8%
42568
 
1.3%
11749
 
0.9%
ValueCountFrequency (%)
076573
40.0%
11749
 
0.9%
23534
 
1.8%
314059
 
7.3%
42568
 
1.3%
59521
 
5.0%
667813
35.4%
715835
 
8.3%
ValueCountFrequency (%)
715835
 
8.3%
667813
35.4%
59521
 
5.0%
42568
 
1.3%
314059
 
7.3%
23534
 
1.8%
11749
 
0.9%
076573
40.0%

D19_SOZIALES
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean1.243590011
Minimum0
Maximum5
Zeros22750
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:58.985212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.158866932
Coefficient of variation (CV)0.9318721779
Kurtosis3.602939994
Mean1.243590011
Median Absolute Deviation (MAD)0
Skewness1.984054596
Sum179021
Variance1.342972565
MonotonicityNot monotonic
2021-12-24T13:27:59.092238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
198964
51.6%
022750
 
11.9%
56405
 
3.3%
45861
 
3.1%
25337
 
2.8%
34638
 
2.4%
(Missing)47697
24.9%
ValueCountFrequency (%)
022750
 
11.9%
198964
51.6%
25337
 
2.8%
34638
 
2.4%
45861
 
3.1%
56405
 
3.3%
ValueCountFrequency (%)
56405
 
3.3%
45861
 
3.1%
34638
 
2.4%
25337
 
2.8%
198964
51.6%
022750
 
11.9%

D19_TECHNIK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.324233507
Minimum0
Maximum7
Zeros117416
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:59.190853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.973774655
Coefficient of variation (CV)1.279464669
Kurtosis-1.633243217
Mean2.324233507
Median Absolute Deviation (MAD)0
Skewness0.5437674919
Sum445444
Variance8.843335698
MonotonicityNot monotonic
2021-12-24T13:27:59.285686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0117416
61.3%
650066
26.1%
716127
 
8.4%
54034
 
2.1%
33362
 
1.8%
4338
 
0.2%
2242
 
0.1%
167
 
< 0.1%
ValueCountFrequency (%)
0117416
61.3%
167
 
< 0.1%
2242
 
0.1%
33362
 
1.8%
4338
 
0.2%
54034
 
2.1%
650066
26.1%
716127
 
8.4%
ValueCountFrequency (%)
716127
 
8.4%
650066
26.1%
54034
 
2.1%
4338
 
0.2%
33362
 
1.8%
2242
 
0.1%
167
 
< 0.1%
0117416
61.3%

D19_TELKO_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04522780874
Minimum0
Maximum6
Zeros184467
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:59.412311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2466779489
Coefficient of variation (CV)5.454121165
Kurtosis56.35872517
Mean0.04522780874
Median Absolute Deviation (MAD)0
Skewness6.60333153
Sum8668
Variance0.06085001046
MonotonicityNot monotonic
2021-12-24T13:27:59.508071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0184467
96.3%
15850
 
3.1%
21231
 
0.6%
373
 
< 0.1%
423
 
< 0.1%
65
 
< 0.1%
53
 
< 0.1%
ValueCountFrequency (%)
0184467
96.3%
15850
 
3.1%
21231
 
0.6%
373
 
< 0.1%
423
 
< 0.1%
53
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
65
 
< 0.1%
53
 
< 0.1%
423
 
< 0.1%
373
 
< 0.1%
21231
 
0.6%
15850
 
3.1%
0184467
96.3%

D19_TELKO_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08687099535
Minimum0
Maximum6
Zeros178411
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:59.616277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3464445385
Coefficient of variation (CV)3.988034639
Kurtosis27.34333128
Mean0.08687099535
Median Absolute Deviation (MAD)0
Skewness4.731110702
Sum16649
Variance0.1200238183
MonotonicityNot monotonic
2021-12-24T13:27:59.716804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0178411
93.1%
110214
 
5.3%
22736
 
1.4%
3217
 
0.1%
463
 
< 0.1%
56
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
0178411
93.1%
110214
 
5.3%
22736
 
1.4%
3217
 
0.1%
463
 
< 0.1%
56
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
65
 
< 0.1%
56
 
< 0.1%
463
 
< 0.1%
3217
 
0.1%
22736
 
1.4%
110214
 
5.3%
0178411
93.1%

D19_TELKO_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.482014276
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:27:59.837430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.288102745
Coefficient of variation (CV)0.1358469528
Kurtosis11.87268648
Mean9.482014276
Median Absolute Deviation (MAD)0
Skewness-3.27854404
Sum1817247
Variance1.659208682
MonotonicityNot monotonic
2021-12-24T13:27:59.941216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10148915
77.7%
921496
 
11.2%
88000
 
4.2%
55042
 
2.6%
73137
 
1.6%
62919
 
1.5%
4877
 
0.5%
1491
 
0.3%
3401
 
0.2%
2374
 
0.2%
ValueCountFrequency (%)
1491
 
0.3%
2374
 
0.2%
3401
 
0.2%
4877
 
0.5%
55042
 
2.6%
62919
 
1.5%
73137
 
1.6%
88000
 
4.2%
921496
 
11.2%
10148915
77.7%
ValueCountFrequency (%)
10148915
77.7%
921496
 
11.2%
88000
 
4.2%
73137
 
1.6%
62919
 
1.5%
55042
 
2.6%
4877
 
0.5%
3401
 
0.2%
2374
 
0.2%
1491
 
0.3%

D19_TELKO_MOBILE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.928693674
Minimum0
Maximum7
Zeros159544
Zeros (%)83.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:00.062890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.124832386
Coefficient of variation (CV)2.287979821
Kurtosis1.867243737
Mean0.928693674
Median Absolute Deviation (MAD)0
Skewness1.931076588
Sum177986
Variance4.514912668
MonotonicityNot monotonic
2021-12-24T13:28:00.167607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0159544
83.2%
622238
 
11.6%
33761
 
2.0%
52680
 
1.4%
72460
 
1.3%
4433
 
0.2%
2387
 
0.2%
1149
 
0.1%
ValueCountFrequency (%)
0159544
83.2%
1149
 
0.1%
2387
 
0.2%
33761
 
2.0%
4433
 
0.2%
52680
 
1.4%
622238
 
11.6%
72460
 
1.3%
ValueCountFrequency (%)
72460
 
1.3%
622238
 
11.6%
52680
 
1.4%
4433
 
0.2%
33761
 
2.0%
2387
 
0.2%
1149
 
0.1%
0159544
83.2%

D19_TELKO_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.799339428
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:00.299254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8326105321
Coefficient of variation (CV)0.08496598553
Kurtosis31.24514428
Mean9.799339428
Median Absolute Deviation (MAD)0
Skewness-5.284351942
Sum1878063
Variance0.6932402981
MonotonicityNot monotonic
2021-12-24T13:28:00.419224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10175675
91.7%
97041
 
3.7%
84044
 
2.1%
52880
 
1.5%
6948
 
0.5%
7606
 
0.3%
4192
 
0.1%
1116
 
0.1%
378
 
< 0.1%
272
 
< 0.1%
ValueCountFrequency (%)
1116
 
0.1%
272
 
< 0.1%
378
 
< 0.1%
4192
 
0.1%
52880
 
1.5%
6948
 
0.5%
7606
 
0.3%
84044
 
2.1%
97041
 
3.7%
10175675
91.7%
ValueCountFrequency (%)
10175675
91.7%
97041
 
3.7%
84044
 
2.1%
7606
 
0.3%
6948
 
0.5%
52880
 
1.5%
4192
 
0.1%
378
 
< 0.1%
272
 
< 0.1%
1116
 
0.1%

D19_TELKO_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.978001795
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:00.525872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2602371818
Coefficient of variation (CV)0.0260810919
Kurtosis354.110959
Mean9.978001795
Median Absolute Deviation (MAD)0
Skewness-16.90329219
Sum1912304
Variance0.06772339079
MonotonicityNot monotonic
2021-12-24T13:28:00.615382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10189516
98.9%
91182
 
0.6%
8482
 
0.3%
7149
 
0.1%
5129
 
0.1%
6125
 
0.1%
436
 
< 0.1%
213
 
< 0.1%
311
 
< 0.1%
19
 
< 0.1%
ValueCountFrequency (%)
19
 
< 0.1%
213
 
< 0.1%
311
 
< 0.1%
436
 
< 0.1%
5129
 
0.1%
6125
 
0.1%
7149
 
0.1%
8482
 
0.3%
91182
 
0.6%
10189516
98.9%
ValueCountFrequency (%)
10189516
98.9%
91182
 
0.6%
8482
 
0.3%
7149
 
0.1%
6125
 
0.1%
5129
 
0.1%
436
 
< 0.1%
311
 
< 0.1%
213
 
< 0.1%
19
 
< 0.1%

D19_TELKO_ONLINE_QUOTE_12
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Memory size1.5 MiB
0.0
143757 
10.0
 
194
5.0
 
3
3.0
 
1

Length

Max length4
Median length3
Mean length3.001347643
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0143757
75.0%
10.0194
 
0.1%
5.03
 
< 0.1%
3.01
 
< 0.1%
(Missing)47697
 
24.9%

Length

2021-12-24T13:28:00.740177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:00.836993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0143757
99.9%
10.0194
 
0.1%
5.03
 
< 0.1%
3.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_TELKO_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6513785403
Minimum0
Maximum7
Zeros168650
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:00.918070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.811244014
Coefficient of variation (CV)2.780632002
Kurtosis4.579967189
Mean0.6513785403
Median Absolute Deviation (MAD)0
Skewness2.523157863
Sum124838
Variance3.28060488
MonotonicityNot monotonic
2021-12-24T13:28:01.020672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0168650
88.0%
614282
 
7.5%
53280
 
1.7%
33180
 
1.7%
71600
 
0.8%
4363
 
0.2%
2257
 
0.1%
140
 
< 0.1%
ValueCountFrequency (%)
0168650
88.0%
140
 
< 0.1%
2257
 
0.1%
33180
 
1.7%
4363
 
0.2%
53280
 
1.7%
614282
 
7.5%
71600
 
0.8%
ValueCountFrequency (%)
71600
 
0.8%
614282
 
7.5%
53280
 
1.7%
4363
 
0.2%
33180
 
1.7%
2257
 
0.1%
140
 
< 0.1%
0168650
88.0%

D19_TIERARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2342474902
Minimum0
Maximum7
Zeros183788
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:01.120908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.167000681
Coefficient of variation (CV)4.981913272
Kurtosis23.33408455
Mean0.2342474902
Median Absolute Deviation (MAD)0
Skewness4.969094841
Sum44894
Variance1.361890588
MonotonicityNot monotonic
2021-12-24T13:28:01.221214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0183788
95.9%
63266
 
1.7%
72524
 
1.3%
31136
 
0.6%
5737
 
0.4%
2129
 
0.1%
469
 
< 0.1%
13
 
< 0.1%
ValueCountFrequency (%)
0183788
95.9%
13
 
< 0.1%
2129
 
0.1%
31136
 
0.6%
469
 
< 0.1%
5737
 
0.4%
63266
 
1.7%
72524
 
1.3%
ValueCountFrequency (%)
72524
 
1.3%
63266
 
1.7%
5737
 
0.4%
469
 
< 0.1%
31136
 
0.6%
2129
 
0.1%
13
 
< 0.1%
0183788
95.9%

D19_VERSAND_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7712833678
Minimum0
Maximum6
Zeros122306
Zeros (%)63.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:01.336217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.254807319
Coefficient of variation (CV)1.626908308
Kurtosis2.23676537
Mean0.7712833678
Median Absolute Deviation (MAD)0
Skewness1.705917564
Sum147818
Variance1.574541407
MonotonicityNot monotonic
2021-12-24T13:28:01.452317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0122306
63.8%
126714
 
13.9%
222076
 
11.5%
39132
 
4.8%
48085
 
4.2%
52818
 
1.5%
6521
 
0.3%
ValueCountFrequency (%)
0122306
63.8%
126714
 
13.9%
222076
 
11.5%
39132
 
4.8%
48085
 
4.2%
52818
 
1.5%
6521
 
0.3%
ValueCountFrequency (%)
6521
 
0.3%
52818
 
1.5%
48085
 
4.2%
39132
 
4.8%
222076
 
11.5%
126714
 
13.9%
0122306
63.8%

D19_VERSAND_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.206285351
Minimum0
Maximum6
Zeros102484
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:01.577980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.622334458
Coefficient of variation (CV)1.344901069
Kurtosis0.3578673553
Mean1.206285351
Median Absolute Deviation (MAD)0
Skewness1.204625579
Sum231187
Variance2.631969093
MonotonicityNot monotonic
2021-12-24T13:28:01.669733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0102484
53.5%
225723
 
13.4%
124973
 
13.0%
414353
 
7.5%
313073
 
6.8%
58139
 
4.2%
62907
 
1.5%
ValueCountFrequency (%)
0102484
53.5%
124973
 
13.0%
225723
 
13.4%
313073
 
6.8%
414353
 
7.5%
58139
 
4.2%
62907
 
1.5%
ValueCountFrequency (%)
62907
 
1.5%
58139
 
4.2%
414353
 
7.5%
313073
 
6.8%
225723
 
13.4%
124973
 
13.0%
0102484
53.5%

D19_VERSAND_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.164167345
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:01.772291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.09421772
Coefficient of variation (CV)0.4319019323
Kurtosis-0.9017408821
Mean7.164167345
Median Absolute Deviation (MAD)1
Skewness-0.7122862189
Sum1373027
Variance9.574183296
MonotonicityNot monotonic
2021-12-24T13:28:02.103520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1073142
38.2%
923459
 
12.2%
521968
 
11.5%
114282
 
7.5%
813490
 
7.0%
211026
 
5.8%
610401
 
5.4%
48820
 
4.6%
78376
 
4.4%
36688
 
3.5%
ValueCountFrequency (%)
114282
 
7.5%
211026
 
5.8%
36688
 
3.5%
48820
 
4.6%
521968
 
11.5%
610401
 
5.4%
78376
 
4.4%
813490
 
7.0%
923459
 
12.2%
1073142
38.2%
ValueCountFrequency (%)
1073142
38.2%
923459
 
12.2%
813490
 
7.0%
78376
 
4.4%
610401
 
5.4%
521968
 
11.5%
48820
 
4.6%
36688
 
3.5%
211026
 
5.8%
114282
 
7.5%

D19_VERSAND_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.691237243
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:02.224486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.987109535
Coefficient of variation (CV)0.2286336778
Kurtosis2.97058246
Mean8.691237243
Median Absolute Deviation (MAD)0
Skewness-1.842252112
Sum1665693
Variance3.948604306
MonotonicityNot monotonic
2021-12-24T13:28:02.312947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10100061
52.2%
936573
 
19.1%
820022
 
10.4%
512124
 
6.3%
77798
 
4.1%
66499
 
3.4%
22676
 
1.4%
42267
 
1.2%
11883
 
1.0%
31749
 
0.9%
ValueCountFrequency (%)
11883
 
1.0%
22676
 
1.4%
31749
 
0.9%
42267
 
1.2%
512124
 
6.3%
66499
 
3.4%
77798
 
4.1%
820022
 
10.4%
936573
 
19.1%
10100061
52.2%
ValueCountFrequency (%)
10100061
52.2%
936573
 
19.1%
820022
 
10.4%
77798
 
4.1%
66499
 
3.4%
512124
 
6.3%
42267
 
1.2%
31749
 
0.9%
22676
 
1.4%
11883
 
1.0%

D19_VERSAND_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.699783983
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:02.425645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.033626886
Coefficient of variation (CV)0.3939885707
Kurtosis-0.4137265446
Mean7.699783983
Median Absolute Deviation (MAD)0
Skewness-1.016201771
Sum1475679
Variance9.202892085
MonotonicityNot monotonic
2021-12-24T13:28:02.519393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1097607
50.9%
918669
 
9.7%
516569
 
8.6%
112310
 
6.4%
28830
 
4.6%
68793
 
4.6%
88782
 
4.6%
47615
 
4.0%
76967
 
3.6%
35510
 
2.9%
ValueCountFrequency (%)
112310
 
6.4%
28830
 
4.6%
35510
 
2.9%
47615
 
4.0%
516569
 
8.6%
68793
 
4.6%
76967
 
3.6%
88782
 
4.6%
918669
 
9.7%
1097607
50.9%
ValueCountFrequency (%)
1097607
50.9%
918669
 
9.7%
88782
 
4.6%
76967
 
3.6%
68793
 
4.6%
516569
 
8.6%
47615
 
4.0%
35510
 
2.9%
28830
 
4.6%
112310
 
6.4%

D19_VERSAND_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean3.216088361
Minimum0
Maximum10
Zeros92458
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:02.638439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.486796355
Coefficient of variation (CV)1.395109789
Kurtosis-1.355994287
Mean3.216088361
Median Absolute Deviation (MAD)0
Skewness0.7516134975
Sum462972
Variance20.13134154
MonotonicityNot monotonic
2021-12-24T13:28:02.735790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
092458
48.2%
1039198
20.5%
53547
 
1.9%
82169
 
1.1%
72016
 
1.1%
31569
 
0.8%
91227
 
0.6%
6517
 
0.3%
2469
 
0.2%
4406
 
0.2%
(Missing)47697
24.9%
ValueCountFrequency (%)
092458
48.2%
1379
 
0.2%
2469
 
0.2%
31569
 
0.8%
4406
 
0.2%
53547
 
1.9%
6517
 
0.3%
72016
 
1.1%
82169
 
1.1%
91227
 
0.6%
ValueCountFrequency (%)
1039198
20.5%
91227
 
0.6%
82169
 
1.1%
72016
 
1.1%
6517
 
0.3%
53547
 
1.9%
4406
 
0.2%
31569
 
0.8%
2469
 
0.2%
1379
 
0.2%

D19_VERSAND_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7767672657
Minimum0
Maximum7
Zeros161199
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:02.835576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.884115822
Coefficient of variation (CV)2.425586022
Kurtosis3.151972912
Mean0.7767672657
Median Absolute Deviation (MAD)0
Skewness2.193928047
Sum148869
Variance3.549892429
MonotonicityNot monotonic
2021-12-24T13:28:02.936306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0161199
84.1%
614194
 
7.4%
37554
 
3.9%
54884
 
2.5%
71610
 
0.8%
2952
 
0.5%
4730
 
0.4%
1529
 
0.3%
ValueCountFrequency (%)
0161199
84.1%
1529
 
0.3%
2952
 
0.5%
37554
 
3.9%
4730
 
0.4%
54884
 
2.5%
614194
 
7.4%
71610
 
0.8%
ValueCountFrequency (%)
71610
 
0.8%
614194
 
7.4%
54884
 
2.5%
4730
 
0.4%
37554
 
3.9%
2952
 
0.5%
1529
 
0.3%
0161199
84.1%

D19_VERSI_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1017469163
Minimum0
Maximum6
Zeros177236
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:03.044507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3933027945
Coefficient of variation (CV)3.865500881
Kurtosis24.98476259
Mean0.1017469163
Median Absolute Deviation (MAD)0
Skewness4.606203053
Sum19500
Variance0.1546870882
MonotonicityNot monotonic
2021-12-24T13:28:03.137629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0177236
92.5%
110135
 
5.3%
23639
 
1.9%
3496
 
0.3%
4133
 
0.1%
511
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
0177236
92.5%
110135
 
5.3%
23639
 
1.9%
3496
 
0.3%
4133
 
0.1%
511
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
511
 
< 0.1%
4133
 
0.1%
3496
 
0.3%
23639
 
1.9%
110135
 
5.3%
0177236
92.5%

D19_VERSI_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1758865026
Minimum0
Maximum6
Zeros168832
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:03.247117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5395389456
Coefficient of variation (CV)3.06754036
Kurtosis15.68757288
Mean0.1758865026
Median Absolute Deviation (MAD)0
Skewness3.678268234
Sum33709
Variance0.2911022738
MonotonicityNot monotonic
2021-12-24T13:28:03.357095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0168832
88.1%
114342
 
7.5%
26691
 
3.5%
31236
 
0.6%
4486
 
0.3%
557
 
< 0.1%
68
 
< 0.1%
ValueCountFrequency (%)
0168832
88.1%
114342
 
7.5%
26691
 
3.5%
31236
 
0.6%
4486
 
0.3%
557
 
< 0.1%
68
 
< 0.1%
ValueCountFrequency (%)
68
 
< 0.1%
557
 
< 0.1%
4486
 
0.3%
31236
 
0.6%
26691
 
3.5%
114342
 
7.5%
0168832
88.1%

D19_VERSI_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.209170789
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:03.465072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.856679741
Coefficient of variation (CV)0.2016120435
Kurtosis6.730387969
Mean9.209170789
Median Absolute Deviation (MAD)0
Skewness-2.691987659
Sum1764956
Variance3.447259662
MonotonicityNot monotonic
2021-12-24T13:28:03.558821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10146758
76.6%
915240
 
8.0%
86834
 
3.6%
56492
 
3.4%
64740
 
2.5%
73664
 
1.9%
23165
 
1.7%
41771
 
0.9%
11691
 
0.9%
31297
 
0.7%
ValueCountFrequency (%)
11691
 
0.9%
23165
 
1.7%
31297
 
0.7%
41771
 
0.9%
56492
 
3.4%
64740
 
2.5%
73664
 
1.9%
86834
 
3.6%
915240
 
8.0%
10146758
76.6%
ValueCountFrequency (%)
10146758
76.6%
915240
 
8.0%
86834
 
3.6%
73664
 
1.9%
64740
 
2.5%
56492
 
3.4%
41771
 
0.9%
31297
 
0.7%
23165
 
1.7%
11691
 
0.9%

D19_VERSI_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.917298019
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:03.662544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5634249934
Coefficient of variation (CV)0.05681234872
Kurtosis67.68284061
Mean9.917298019
Median Absolute Deviation (MAD)0
Skewness-7.986854681
Sum1900670
Variance0.3174477232
MonotonicityNot monotonic
2021-12-24T13:28:03.769257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10185928
97.0%
92136
 
1.1%
51723
 
0.9%
81216
 
0.6%
6365
 
0.2%
7197
 
0.1%
437
 
< 0.1%
224
 
< 0.1%
316
 
< 0.1%
110
 
< 0.1%
ValueCountFrequency (%)
110
 
< 0.1%
224
 
< 0.1%
316
 
< 0.1%
437
 
< 0.1%
51723
 
0.9%
6365
 
0.2%
7197
 
0.1%
81216
 
0.6%
92136
 
1.1%
10185928
97.0%
ValueCountFrequency (%)
10185928
97.0%
92136
 
1.1%
81216
 
0.6%
7197
 
0.1%
6365
 
0.2%
51723
 
0.9%
437
 
< 0.1%
316
 
< 0.1%
224
 
< 0.1%
110
 
< 0.1%

D19_VERSI_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.98316219
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:03.888399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2613317165
Coefficient of variation (CV)0.02617724841
Kurtosis430.3124448
Mean9.98316219
Median Absolute Deviation (MAD)0
Skewness-19.37376118
Sum1913293
Variance0.06829426607
MonotonicityNot monotonic
2021-12-24T13:28:04.004089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10190509
99.4%
9394
 
0.2%
8212
 
0.1%
7172
 
0.1%
5165
 
0.1%
6107
 
0.1%
449
 
< 0.1%
319
 
< 0.1%
113
 
< 0.1%
212
 
< 0.1%
ValueCountFrequency (%)
113
 
< 0.1%
212
 
< 0.1%
319
 
< 0.1%
449
 
< 0.1%
5165
 
0.1%
6107
 
0.1%
7172
 
0.1%
8212
 
0.1%
9394
 
0.2%
10190509
99.4%
ValueCountFrequency (%)
10190509
99.4%
9394
 
0.2%
8212
 
0.1%
7172
 
0.1%
6107
 
0.1%
5165
 
0.1%
449
 
< 0.1%
319
 
< 0.1%
212
 
< 0.1%
113
 
< 0.1%

D19_VERSI_ONLINE_QUOTE_12
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing47697
Missing (%)24.9%
Memory size1.5 MiB
0.0
143697 
10.0
 
245
5.0
 
11
7.0
 
2

Length

Max length4
Median length3
Mean length3.001701921
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0143697
75.0%
10.0245
 
0.1%
5.011
 
< 0.1%
7.02
 
< 0.1%
(Missing)47697
 
24.9%

Length

2021-12-24T13:28:04.126184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:04.206528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0143697
99.8%
10.0245
 
0.2%
5.011
 
< 0.1%
7.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_VERSICHERUNGEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.168612903
Minimum0
Maximum7
Zeros144720
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:04.286930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.19925685
Coefficient of variation (CV)1.881937847
Kurtosis0.6245897634
Mean1.168612903
Median Absolute Deviation (MAD)0
Skewness1.533363389
Sum223967
Variance4.836730693
MonotonicityNot monotonic
2021-12-24T13:28:04.381293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0144720
75.5%
622074
 
11.5%
310135
 
5.3%
56224
 
3.2%
22731
 
1.4%
42180
 
1.1%
72038
 
1.1%
11550
 
0.8%
ValueCountFrequency (%)
0144720
75.5%
11550
 
0.8%
22731
 
1.4%
310135
 
5.3%
42180
 
1.1%
56224
 
3.2%
622074
 
11.5%
72038
 
1.1%
ValueCountFrequency (%)
72038
 
1.1%
622074
 
11.5%
56224
 
3.2%
42180
 
1.1%
310135
 
5.3%
22731
 
1.4%
11550
 
0.8%
0144720
75.5%

D19_VOLLSORTIMENT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.365892347
Minimum0
Maximum7
Zeros108259
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:04.485015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.841913651
Coefficient of variation (CV)1.201201591
Kurtosis-1.620472871
Mean2.365892347
Median Absolute Deviation (MAD)0
Skewness0.4846577738
Sum453428
Variance8.076473199
MonotonicityNot monotonic
2021-12-24T13:28:04.578763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0108259
56.5%
649475
25.8%
311622
 
6.1%
710499
 
5.5%
57668
 
4.0%
22051
 
1.1%
41233
 
0.6%
1845
 
0.4%
ValueCountFrequency (%)
0108259
56.5%
1845
 
0.4%
22051
 
1.1%
311622
 
6.1%
41233
 
0.6%
57668
 
4.0%
649475
25.8%
710499
 
5.5%
ValueCountFrequency (%)
710499
 
5.5%
649475
25.8%
57668
 
4.0%
41233
 
0.6%
311622
 
6.1%
22051
 
1.1%
1845
 
0.4%
0108259
56.5%

D19_WEIN_FEINKOST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7881263958
Minimum0
Maximum7
Zeros166431
Zeros (%)86.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:04.680492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.065434151
Coefficient of variation (CV)2.620688969
Kurtosis3.500394388
Mean0.7881263958
Median Absolute Deviation (MAD)0
Skewness2.312607798
Sum151046
Variance4.266018233
MonotonicityNot monotonic
2021-12-24T13:28:04.766464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0166431
86.8%
612974
 
6.8%
78408
 
4.4%
32107
 
1.1%
51425
 
0.7%
4150
 
0.1%
2143
 
0.1%
114
 
< 0.1%
ValueCountFrequency (%)
0166431
86.8%
114
 
< 0.1%
2143
 
0.1%
32107
 
1.1%
4150
 
0.1%
51425
 
0.7%
612974
 
6.8%
78408
 
4.4%
ValueCountFrequency (%)
78408
 
4.4%
612974
 
6.8%
51425
 
0.7%
4150
 
0.1%
32107
 
1.1%
2143
 
0.1%
114
 
< 0.1%
0166431
86.8%

DSL_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Memory size1.5 MiB
1.0
138494 
0.0
 
3231

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0138494
72.3%
0.03231
 
1.7%
(Missing)49927
 
26.1%

Length

2021-12-24T13:28:04.907699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:04.988154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0138494
97.7%
0.03231
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EINGEFUEGT_AM
Categorical

HIGH CARDINALITY
MISSING

Distinct3034
Distinct (%)2.1%
Missing49927
Missing (%)26.1%
Memory size1.5 MiB
1992-02-10 00:00:00
64744 
1992-02-12 00:00:00
43686 
2003-11-18 00:00:00
 
1066
2005-12-16 00:00:00
 
808
1995-02-07 00:00:00
 
569
Other values (3029)
30852 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1029 ?
Unique (%)0.7%

Sample

1st row1992-02-12 00:00:00
2nd row1992-02-10 00:00:00
3rd row1992-02-10 00:00:00
4th row1992-02-12 00:00:00
5th row1992-02-10 00:00:00

Common Values

ValueCountFrequency (%)
1992-02-10 00:00:0064744
33.8%
1992-02-12 00:00:0043686
22.8%
2003-11-18 00:00:001066
 
0.6%
2005-12-16 00:00:00808
 
0.4%
1995-02-07 00:00:00569
 
0.3%
2004-04-14 00:00:00443
 
0.2%
2005-08-23 00:00:00391
 
0.2%
2005-04-15 00:00:00363
 
0.2%
1993-03-01 00:00:00357
 
0.2%
1995-10-10 00:00:00317
 
0.2%
Other values (3024)28981
15.1%
(Missing)49927
26.1%

Length

2021-12-24T13:28:05.087198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00141725
50.0%
1992-02-1064744
22.8%
1992-02-1243686
 
15.4%
2003-11-181066
 
0.4%
2005-12-16808
 
0.3%
1995-02-07569
 
0.2%
2004-04-14443
 
0.2%
2005-08-23391
 
0.1%
2005-04-15363
 
0.1%
1993-03-01357
 
0.1%
Other values (3025)29298
 
10.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EINGEZOGENAM_HH_JAHR
Real number (ℝ≥0)

MISSING

Distinct33
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean1999.185053
Minimum1986
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:05.209868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1986
5-th percentile1994
Q11994
median1997
Q32004
95-th percentile2012
Maximum2018
Range32
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.17809944
Coefficient of variation (CV)0.00309030894
Kurtosis-0.05160858788
Mean1999.185053
Median Absolute Deviation (MAD)3
Skewness1.019184149
Sum289993787
Variance38.1689127
MonotonicityNot monotonic
2021-12-24T13:28:05.318133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
199459559
31.1%
199716786
 
8.8%
20049028
 
4.7%
20015089
 
2.7%
19994920
 
2.6%
20004911
 
2.6%
19984717
 
2.5%
20024682
 
2.4%
20074074
 
2.1%
20083127
 
1.6%
Other values (23)28163
14.7%
(Missing)46596
24.3%
ValueCountFrequency (%)
19867
 
< 0.1%
198717
 
< 0.1%
198834
 
< 0.1%
198984
 
< 0.1%
1990124
 
0.1%
1991145
 
0.1%
1992320
 
0.2%
1993458
 
0.2%
199459559
31.1%
19951731
 
0.9%
ValueCountFrequency (%)
201883
 
< 0.1%
201729
 
< 0.1%
2016336
 
0.2%
20152549
1.3%
20142281
1.2%
20131739
0.9%
20122313
1.2%
20112263
1.2%
20101880
1.0%
20092275
1.2%

EWDICHTE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing49959
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean3.88170199
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:05.428825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.607620711
Coefficient of variation (CV)0.4141535633
Kurtosis-1.150113125
Mean3.88170199
Median Absolute Deviation (MAD)1
Skewness-0.2725504899
Sum550010
Variance2.584444349
MonotonicityNot monotonic
2021-12-24T13:28:05.529420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
532475
16.9%
627363
14.3%
427170
14.2%
225546
13.3%
317273
 
9.0%
111866
 
6.2%
(Missing)49959
26.1%
ValueCountFrequency (%)
111866
 
6.2%
225546
13.3%
317273
9.0%
427170
14.2%
532475
16.9%
627363
14.3%
ValueCountFrequency (%)
627363
14.3%
532475
16.9%
427170
14.2%
317273
9.0%
225546
13.3%
111866
 
6.2%

EXTSEL992
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)0.1%
Missing85283
Missing (%)44.5%
Infinite0
Infinite (%)0.0%
Mean38.4185994
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:05.670339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q129
median36
Q353
95-th percentile56
Maximum56
Range55
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.68946562
Coefficient of variation (CV)0.3563239116
Kurtosis-0.4832495904
Mean38.4185994
Median Absolute Deviation (MAD)11
Skewness-0.4013270401
Sum4086548
Variance187.4014689
MonotonicityNot monotonic
2021-12-24T13:28:05.811045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5613656
 
7.1%
557324
 
3.8%
366436
 
3.4%
316299
 
3.3%
235991
 
3.1%
355546
 
2.9%
345147
 
2.7%
504434
 
2.3%
274207
 
2.2%
533373
 
1.8%
Other values (46)43956
22.9%
(Missing)85283
44.5%
ValueCountFrequency (%)
1253
 
0.1%
2517
0.3%
3847
0.4%
4292
 
0.2%
5267
 
0.1%
6990
0.5%
7110
 
0.1%
8123
 
0.1%
9183
 
0.1%
10334
 
0.2%
ValueCountFrequency (%)
5613656
7.1%
557324
3.8%
543279
 
1.7%
533373
 
1.8%
521082
 
0.6%
51352
 
0.2%
504434
 
2.3%
49100
 
0.1%
482271
 
1.2%
471211
 
0.6%

FINANZ_ANLEGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
94437 
5
50216 
2
26488 
3
12943 
4
 
7568

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row4

Common Values

ValueCountFrequency (%)
194437
49.3%
550216
26.2%
226488
 
13.8%
312943
 
6.8%
47568
 
3.9%

Length

2021-12-24T13:28:05.960931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:06.052382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
194437
49.3%
550216
26.2%
226488
 
13.8%
312943
 
6.8%
47568
 
3.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_HAUSBAUER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
72139 
2
53705 
1
25340 
5
21979 
4
18489 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
372139
37.6%
253705
28.0%
125340
 
13.2%
521979
 
11.5%
418489
 
9.6%

Length

2021-12-24T13:28:06.162915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:06.261373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
372139
37.6%
253705
28.0%
125340
 
13.2%
521979
 
11.5%
418489
 
9.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_MINIMALIST
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
84449 
3
66439 
4
29162 
2
9853 
1
 
1749

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row3

Common Values

ValueCountFrequency (%)
584449
44.1%
366439
34.7%
429162
 
15.2%
29853
 
5.1%
11749
 
0.9%

Length

2021-12-24T13:28:06.374133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:06.469179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
584449
44.1%
366439
34.7%
429162
 
15.2%
29853
 
5.1%
11749
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_SPARER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
105240 
4
50692 
2
24752 
3
 
9057
5
 
1911

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1105240
54.9%
450692
26.5%
224752
 
12.9%
39057
 
4.7%
51911
 
1.0%

Length

2021-12-24T13:28:06.636358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:06.742368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1105240
54.9%
450692
26.5%
224752
 
12.9%
39057
 
4.7%
51911
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_UNAUFFAELLIGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
61387 
2
51830 
5
51104 
3
22746 
4
 
4585

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row1
5th row5

Common Values

ValueCountFrequency (%)
161387
32.0%
251830
27.0%
551104
26.7%
322746
 
11.9%
44585
 
2.4%

Length

2021-12-24T13:28:06.873853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:06.981172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
161387
32.0%
251830
27.0%
551104
26.7%
322746
 
11.9%
44585
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_VORSORGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
104286 
3
54370 
4
26954 
2
 
3343
1
 
2699

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5104286
54.4%
354370
28.4%
426954
 
14.1%
23343
 
1.7%
12699
 
1.4%

Length

2021-12-24T13:28:07.074287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:07.158901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5104286
54.4%
354370
28.4%
426954
 
14.1%
23343
 
1.7%
12699
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZTYP
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.137958383
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:07.260267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.503945795
Coefficient of variation (CV)0.3634511649
Kurtosis-1.093061913
Mean4.137958383
Median Absolute Deviation (MAD)1
Skewness-0.3987347994
Sum793048
Variance2.261852953
MonotonicityNot monotonic
2021-12-24T13:28:07.359009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
448442
25.3%
546792
24.4%
244277
23.1%
642831
22.3%
35235
 
2.7%
14075
 
2.1%
ValueCountFrequency (%)
14075
 
2.1%
244277
23.1%
35235
 
2.7%
448442
25.3%
546792
24.4%
642831
22.3%
ValueCountFrequency (%)
642831
22.3%
546792
24.4%
448442
25.3%
35235
 
2.7%
244277
23.1%
14075
 
2.1%

FIRMENDICHTE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Memory size1.5 MiB
4.0
54008 
3.0
35438 
5.0
28282 
2.0
19625 
1.0
 
4372

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.054008
28.2%
3.035438
18.5%
5.028282
14.8%
2.019625
 
10.2%
1.04372
 
2.3%
(Missing)49927
26.1%

Length

2021-12-24T13:28:07.495700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:07.584296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.054008
38.1%
3.035438
25.0%
5.028282
20.0%
2.019625
 
13.8%
1.04372
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GEBAEUDETYP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean2.369941789
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:07.706926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.434226862
Coefficient of variation (CV)1.02712517
Kurtosis1.207693379
Mean2.369941789
Median Absolute Deviation (MAD)0
Skewness1.674515617
Sum335880
Variance5.925460416
MonotonicityNot monotonic
2021-12-24T13:28:07.797503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
195145
49.6%
323655
 
12.3%
820475
 
10.7%
22057
 
1.1%
4251
 
0.1%
6142
 
0.1%
(Missing)49927
26.1%
ValueCountFrequency (%)
195145
49.6%
22057
 
1.1%
323655
 
12.3%
4251
 
0.1%
6142
 
0.1%
820475
 
10.7%
ValueCountFrequency (%)
820475
 
10.7%
6142
 
0.1%
4251
 
0.1%
323655
 
12.3%
22057
 
1.1%
195145
49.6%

GEBAEUDETYP_RASTER
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Memory size1.5 MiB
4.0
74249 
3.0
30875 
5.0
28282 
2.0
 
6649
1.0
 
1670

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.074249
38.7%
3.030875
16.1%
5.028282
 
14.8%
2.06649
 
3.5%
1.01670
 
0.9%
(Missing)49927
26.1%

Length

2021-12-24T13:28:07.938219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:08.038909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.074249
52.4%
3.030875
21.8%
5.028282
 
20.0%
2.06649
 
4.7%
1.01670
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GEBURTSJAHR
Real number (ℝ≥0)

ZEROS

Distinct113
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.392733
Minimum0
Maximum2017
Zeros93024
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:08.169622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1926
Q31949
95-th percentile1970
Maximum2017
Range2017
Interquartile range (IQR)1949

Descriptive statistics

Standard deviation974.5310809
Coefficient of variation (CV)0.971235937
Kurtosis-1.996152602
Mean1003.392733
Median Absolute Deviation (MAD)54
Skewness-0.05814304244
Sum192302224
Variance949710.8276
MonotonicityNot monotonic
2021-12-24T13:28:08.308105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
093024
48.5%
19413535
 
1.8%
19403085
 
1.6%
19393071
 
1.6%
19432875
 
1.5%
19422802
 
1.5%
19382775
 
1.4%
19442625
 
1.4%
19372603
 
1.4%
19362443
 
1.3%
Other values (103)72814
38.0%
ValueCountFrequency (%)
093024
48.5%
19003
 
< 0.1%
19021
 
< 0.1%
19081
 
< 0.1%
19091
 
< 0.1%
19106
 
< 0.1%
191110
 
< 0.1%
19127
 
< 0.1%
19137
 
< 0.1%
191413
 
< 0.1%
ValueCountFrequency (%)
201745
< 0.1%
20167
 
< 0.1%
201550
< 0.1%
201419
 
< 0.1%
201339
< 0.1%
201268
< 0.1%
20114
 
< 0.1%
20102
 
< 0.1%
20092
 
< 0.1%
20086
 
< 0.1%

GEMEINDETYP
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing50476
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean24.77668301
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:08.448794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median22
Q330
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.75850998
Coefficient of variation (CV)0.4745796675
Kurtosis-0.6734788608
Mean24.77668301
Median Absolute Deviation (MAD)10
Skewness0.5624746734
Sum3497873
Variance138.262557
MonotonicityNot monotonic
2021-12-24T13:28:08.551415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2229808
15.6%
3024748
12.9%
1222494
11.7%
4021736
11.3%
1119992
 
10.4%
2113087
 
6.8%
509311
 
4.9%
(Missing)50476
26.3%
ValueCountFrequency (%)
1119992
10.4%
1222494
11.7%
2113087
6.8%
2229808
15.6%
3024748
12.9%
4021736
11.3%
509311
 
4.9%
ValueCountFrequency (%)
509311
 
4.9%
4021736
11.3%
3024748
12.9%
2229808
15.6%
2113087
6.8%
1222494
11.7%
1119992
10.4%

GFK_URLAUBERTYP
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean6.302267577
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:08.690151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median5
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.877181228
Coefficient of variation (CV)0.4565311124
Kurtosis-0.8363825052
Mean6.302267577
Median Absolute Deviation (MAD)2
Skewness0.2531100866
Sum1187593
Variance8.278171818
MonotonicityNot monotonic
2021-12-24T13:28:08.792667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
558113
30.3%
1027291
14.2%
817631
 
9.2%
416361
 
8.5%
314684
 
7.7%
713826
 
7.2%
18782
 
4.6%
117486
 
3.9%
127303
 
3.8%
66373
 
3.3%
Other values (2)10589
 
5.5%
ValueCountFrequency (%)
18782
 
4.6%
25073
 
2.6%
314684
 
7.7%
416361
 
8.5%
558113
30.3%
66373
 
3.3%
713826
 
7.2%
817631
 
9.2%
95516
 
2.9%
1027291
14.2%
ValueCountFrequency (%)
127303
 
3.8%
117486
 
3.9%
1027291
14.2%
95516
 
2.9%
817631
 
9.2%
713826
 
7.2%
66373
 
3.3%
558113
30.3%
416361
 
8.5%
314684
 
7.7%

GREEN_AVANTGARDE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
121283 
1
70369 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0121283
63.3%
170369
36.7%

Length

2021-12-24T13:28:09.245358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:09.335782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0121283
63.3%
170369
36.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HEALTH_TYP
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
56955 
-1
48990 
1
46183 
3
39524 

Length

Max length2
Median length1
Mean length1.25561956
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
256955
29.7%
-148990
25.6%
146183
24.1%
339524
20.6%

Length

2021-12-24T13:28:09.446411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:09.536867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
195173
49.7%
256955
29.7%
339524
20.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HH_DELTA_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing53742
Missing (%)28.0%
Memory size1.5 MiB
0.0
117263 
1.0
20647 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0117263
61.2%
1.020647
 
10.8%
(Missing)53742
28.0%

Length

2021-12-24T13:28:09.657522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:09.747993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0117263
85.0%
1.020647
 
15.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HH_EINKOMMEN_SCORE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing2968
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.942480549
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:09.828411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.533347234
Coefficient of variation (CV)0.5211070077
Kurtosis-0.8746764077
Mean2.942480549
Median Absolute Deviation (MAD)1
Skewness0.5570660097
Sum555199
Variance2.351153741
MonotonicityNot monotonic
2021-12-24T13:28:09.939009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
270160
36.6%
129936
15.6%
427674
 
14.4%
523923
 
12.5%
322438
 
11.7%
614553
 
7.6%
(Missing)2968
 
1.5%
ValueCountFrequency (%)
129936
15.6%
270160
36.6%
322438
 
11.7%
427674
 
14.4%
523923
 
12.5%
614553
 
7.6%
ValueCountFrequency (%)
614553
 
7.6%
523923
 
12.5%
427674
 
14.4%
322438
 
11.7%
270160
36.6%
129936
15.6%

INNENSTADT
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)< 0.1%
Missing49959
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean4.784576514
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:10.057695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.96147294
Coefficient of variation (CV)0.409957482
Kurtosis-0.8055497713
Mean4.784576514
Median Absolute Deviation (MAD)1
Skewness0.005190446354
Sum677941
Variance3.847376094
MonotonicityNot monotonic
2021-12-24T13:28:10.148149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
528031
14.6%
427700
14.5%
619532
 
10.2%
818075
 
9.4%
315792
 
8.2%
214371
 
7.5%
711814
 
6.2%
16378
 
3.3%
(Missing)49959
26.1%
ValueCountFrequency (%)
16378
 
3.3%
214371
7.5%
315792
8.2%
427700
14.5%
528031
14.6%
619532
10.2%
711814
6.2%
818075
9.4%
ValueCountFrequency (%)
818075
9.4%
711814
6.2%
619532
10.2%
528031
14.6%
427700
14.5%
315792
8.2%
214371
7.5%
16378
 
3.3%

KBA05_ALTER1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean1.592075004
Minimum0
Maximum9
Zeros22932
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:10.250693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.201312382
Coefficient of variation (CV)0.7545576553
Kurtosis8.267882678
Mean1.592075004
Median Absolute Deviation (MAD)1
Skewness1.678464283
Sum216000
Variance1.44315144
MonotonicityNot monotonic
2021-12-24T13:28:10.341158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
144232
23.1%
243590
22.7%
022932
12.0%
319749
 
10.3%
44236
 
2.2%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
022932
12.0%
144232
23.1%
243590
22.7%
319749
10.3%
44236
 
2.2%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
44236
 
2.2%
319749
10.3%
243590
22.7%
144232
23.1%
022932
12.0%

KBA05_ALTER2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.797548499
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:10.441718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.138182474
Coefficient of variation (CV)0.4068499524
Kurtosis4.736317378
Mean2.797548499
Median Absolute Deviation (MAD)1
Skewness1.144628188
Sum379549
Variance1.295459344
MonotonicityNot monotonic
2021-12-24T13:28:10.532304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
352638
27.5%
238751
20.2%
421474
 
11.2%
114885
 
7.8%
56991
 
3.6%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
114885
 
7.8%
238751
20.2%
352638
27.5%
421474
11.2%
56991
 
3.6%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
56991
 
3.6%
421474
11.2%
352638
27.5%
238751
20.2%
114885
 
7.8%

KBA05_ALTER3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.170506811
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:10.632860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.144634922
Coefficient of variation (CV)0.3610258518
Kurtosis3.380510694
Mean3.170506811
Median Absolute Deviation (MAD)1
Skewness0.79725924
Sum430149
Variance1.310189104
MonotonicityNot monotonic
2021-12-24T13:28:10.723290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
354227
28.3%
431966
16.7%
226013
13.6%
514162
 
7.4%
18371
 
4.4%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
18371
 
4.4%
226013
13.6%
354227
28.3%
431966
16.7%
514162
 
7.4%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
514162
 
7.4%
431966
16.7%
354227
28.3%
226013
13.6%
18371
 
4.4%

KBA05_ALTER4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.315606757
Minimum0
Maximum9
Zeros2435
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:10.854062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197247528
Coefficient of variation (CV)0.3610945493
Kurtosis2.936314364
Mean3.315606757
Median Absolute Deviation (MAD)1
Skewness0.2757794943
Sum449835
Variance1.433401643
MonotonicityNot monotonic
2021-12-24T13:28:10.954803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
353439
27.9%
437377
19.5%
518294
 
9.5%
216949
 
8.8%
16245
 
3.3%
02435
 
1.3%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
02435
 
1.3%
16245
 
3.3%
216949
 
8.8%
353439
27.9%
437377
19.5%
518294
 
9.5%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
518294
 
9.5%
437377
19.5%
353439
27.9%
216949
 
8.8%
16245
 
3.3%
02435
 
1.3%

KBA05_ANHANG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
1.0
65089 
0.0
36170 
3.0
18018 
2.0
15524 
9.0
 
871

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.065089
34.0%
0.036170
18.9%
3.018018
 
9.4%
2.015524
 
8.1%
9.0871
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:11.075521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:11.166023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.065089
48.0%
0.036170
26.7%
3.018018
 
13.3%
2.015524
 
11.4%
9.0871
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
3.0
33109 
4.0
30408 
2.0
28787 
0.0
22465 
1.0
20903 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.033109
17.3%
4.030408
15.9%
2.028787
15.0%
0.022465
11.7%
1.020903
 
10.9%
(Missing)55980
29.2%

Length

2021-12-24T13:28:11.284564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:11.356924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.033109
24.4%
4.030408
22.4%
2.028787
21.2%
0.022465
16.6%
1.020903
15.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
0.0
43729 
1.0
42079 
2.0
30513 
3.0
16481 
4.0
 
2870

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row0.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.043729
22.8%
1.042079
22.0%
2.030513
15.9%
3.016481
 
8.6%
4.02870
 
1.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:11.497747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:11.602985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.043729
32.2%
1.042079
31.0%
2.030513
22.5%
3.016481
 
12.1%
4.02870
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
0.0
112655 
1.0
 
10163
2.0
 
6671
3.0
 
6183

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0112655
58.8%
1.010163
 
5.3%
2.06671
 
3.5%
3.06183
 
3.2%
(Missing)55980
29.2%

Length

2021-12-24T13:28:11.718905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:11.803526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0112655
83.0%
1.010163
 
7.5%
2.06671
 
4.9%
3.06183
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
0.0
121487 
1.0
 
7560
2.0
 
6625

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0121487
63.4%
1.07560
 
3.9%
2.06625
 
3.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:11.903760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:11.972754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0121487
89.5%
1.07560
 
5.6%
2.06625
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_AUTOQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.585139159
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:12.057366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.081234251
Coefficient of variation (CV)0.3015878054
Kurtosis3.504972143
Mean3.585139159
Median Absolute Deviation (MAD)1
Skewness0.3952850565
Sum486403
Variance1.169067506
MonotonicityNot monotonic
2021-12-24T13:28:12.155993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
452193
27.2%
344460
23.2%
521682
 
11.3%
211032
 
5.8%
15371
 
2.8%
9934
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
15371
 
2.8%
211032
 
5.8%
344460
23.2%
452193
27.2%
521682
11.3%
9934
 
0.5%
ValueCountFrequency (%)
9934
 
0.5%
521682
11.3%
452193
27.2%
344460
23.2%
211032
 
5.8%
15371
 
2.8%

KBA05_BAUMAX
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean1.043826287
Minimum0
Maximum5
Zeros53555
Zeros (%)27.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:12.257577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.384973137
Coefficient of variation (CV)1.326823395
Kurtosis2.626944134
Mean1.043826287
Median Absolute Deviation (MAD)1
Skewness1.863135698
Sum141618
Variance1.918150591
MonotonicityNot monotonic
2021-12-24T13:28:12.351308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
163408
33.1%
053555
27.9%
59981
 
5.2%
34877
 
2.5%
42986
 
1.6%
2865
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
053555
27.9%
163408
33.1%
2865
 
0.5%
34877
 
2.5%
42986
 
1.6%
59981
 
5.2%
ValueCountFrequency (%)
59981
 
5.2%
42986
 
1.6%
34877
 
2.5%
2865
 
0.5%
163408
33.1%
053555
27.9%

KBA05_CCM1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.812923816
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:12.457647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.098849925
Coefficient of variation (CV)0.3906433294
Kurtosis5.601212496
Mean2.812923816
Median Absolute Deviation (MAD)1
Skewness1.227273526
Sum381635
Variance1.207471157
MonotonicityNot monotonic
2021-12-24T13:28:12.557886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
356364
29.4%
238167
19.9%
421340
 
11.1%
112972
 
6.8%
55896
 
3.1%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
112972
 
6.8%
238167
19.9%
356364
29.4%
421340
 
11.1%
55896
 
3.1%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
55896
 
3.1%
421340
 
11.1%
356364
29.4%
238167
19.9%
112972
 
6.8%

KBA05_CCM2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.940783655
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:12.673750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.086483205
Coefficient of variation (CV)0.3694536328
Kurtosis5.377674774
Mean2.940783655
Median Absolute Deviation (MAD)1
Skewness1.121395826
Sum398982
Variance1.180445754
MonotonicityNot monotonic
2021-12-24T13:28:12.758359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
358873
30.7%
232678
17.1%
426130
13.6%
110300
 
5.4%
56758
 
3.5%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
110300
 
5.4%
232678
17.1%
358873
30.7%
426130
13.6%
56758
 
3.5%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
56758
 
3.5%
426130
13.6%
358873
30.7%
232678
17.1%
110300
 
5.4%

KBA05_CCM3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.276630403
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:12.889843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.081889258
Coefficient of variation (CV)0.3301834887
Kurtosis4.201918951
Mean3.276630403
Median Absolute Deviation (MAD)1
Skewness0.8656901725
Sum444547
Variance1.170484366
MonotonicityNot monotonic
2021-12-24T13:28:12.990082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
357173
29.8%
436757
19.2%
221466
 
11.2%
513832
 
7.2%
15511
 
2.9%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
15511
 
2.9%
221466
 
11.2%
357173
29.8%
436757
19.2%
513832
 
7.2%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
513832
 
7.2%
436757
19.2%
357173
29.8%
221466
 
11.2%
15511
 
2.9%

KBA05_CCM4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean1.544423315
Minimum0
Maximum9
Zeros32427
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:13.121508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.374065005
Coefficient of variation (CV)0.889694549
Kurtosis4.387002818
Mean1.544423315
Median Absolute Deviation (MAD)1
Skewness1.389649065
Sum209535
Variance1.888054637
MonotonicityNot monotonic
2021-12-24T13:28:13.237364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
144675
23.3%
032427
16.9%
227960
14.6%
318165
 
9.5%
411512
 
6.0%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
032427
16.9%
144675
23.3%
227960
14.6%
318165
9.5%
411512
 
6.0%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
411512
 
6.0%
318165
9.5%
227960
14.6%
144675
23.3%
032427
16.9%

KBA05_DIESEL
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.229420956
Minimum0
Maximum9
Zeros5076
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:13.352962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.109139093
Coefficient of variation (CV)0.4975009719
Kurtosis8.074574473
Mean2.229420956
Median Absolute Deviation (MAD)1
Skewness1.461198503
Sum302470
Variance1.230189527
MonotonicityNot monotonic
2021-12-24T13:28:13.459760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
258062
30.3%
334964
18.2%
124497
12.8%
412140
 
6.3%
05076
 
2.6%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
05076
 
2.6%
124497
12.8%
258062
30.3%
334964
18.2%
412140
 
6.3%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
412140
 
6.3%
334964
18.2%
258062
30.3%
124497
12.8%
05076
 
2.6%

KBA05_FRAU
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.097639896
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:13.559997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.100045746
Coefficient of variation (CV)0.3551238306
Kurtosis4.498935757
Mean3.097639896
Median Absolute Deviation (MAD)1
Skewness0.9893713375
Sum420263
Variance1.210100643
MonotonicityNot monotonic
2021-12-24T13:28:13.660237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
359831
31.2%
428830
15.0%
227087
14.1%
510972
 
5.7%
18019
 
4.2%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
18019
 
4.2%
227087
14.1%
359831
31.2%
428830
15.0%
510972
 
5.7%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
510972
 
5.7%
428830
15.0%
359831
31.2%
227087
14.1%
18019
 
4.2%

KBA05_GBZ
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.630402736
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:13.776113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.168496161
Coefficient of variation (CV)0.3218640592
Kurtosis-0.3469478714
Mean3.630402736
Median Absolute Deviation (MAD)1
Skewness-0.6267723131
Sum492544
Variance1.365383279
MonotonicityNot monotonic
2021-12-24T13:28:13.891975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
443018
22.4%
536629
19.1%
334714
18.1%
211876
 
6.2%
19433
 
4.9%
02
 
< 0.1%
(Missing)55980
29.2%
ValueCountFrequency (%)
02
 
< 0.1%
19433
 
4.9%
211876
 
6.2%
334714
18.1%
443018
22.4%
536629
19.1%
ValueCountFrequency (%)
536629
19.1%
443018
22.4%
334714
18.1%
211876
 
6.2%
19433
 
4.9%
02
 
< 0.1%

KBA05_HERST1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.791873047
Minimum0
Maximum9
Zeros5354
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:14.023455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.382876588
Coefficient of variation (CV)0.4953221602
Kurtosis1.626104281
Mean2.791873047
Median Absolute Deviation (MAD)1
Skewness0.5606794931
Sum378779
Variance1.912347659
MonotonicityNot monotonic
2021-12-24T13:28:14.123693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
238948
20.3%
338091
19.9%
421593
 
11.3%
115481
 
8.1%
515272
 
8.0%
05354
 
2.8%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
05354
 
2.8%
115481
 
8.1%
238948
20.3%
338091
19.9%
421593
11.3%
515272
 
8.0%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
515272
 
8.0%
421593
11.3%
338091
19.9%
238948
20.3%
115481
 
8.1%
05354
 
2.8%

KBA05_HERST2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.186457043
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:14.223928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.081791869
Coefficient of variation (CV)0.3394967685
Kurtosis4.485782146
Mean3.186457043
Median Absolute Deviation (MAD)1
Skewness0.9951617215
Sum432313
Variance1.170273649
MonotonicityNot monotonic
2021-12-24T13:28:14.324167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
357977
30.3%
432809
17.1%
226240
13.7%
512139
 
6.3%
15574
 
2.9%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
15574
 
2.9%
226240
13.7%
357977
30.3%
432809
17.1%
512139
 
6.3%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
512139
 
6.3%
432809
17.1%
357977
30.3%
226240
13.7%
15574
 
2.9%

KBA05_HERST3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.832507813
Minimum0
Maximum9
Zeros1918
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:14.477490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.183915028
Coefficient of variation (CV)0.4179741439
Kurtosis3.972805518
Mean2.832507813
Median Absolute Deviation (MAD)1
Skewness0.8177614901
Sum384292
Variance1.401654794
MonotonicityNot monotonic
2021-12-24T13:28:14.593346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
354606
28.5%
233031
17.2%
423549
12.3%
114089
 
7.4%
57546
 
3.9%
01918
 
1.0%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
01918
 
1.0%
114089
 
7.4%
233031
17.2%
354606
28.5%
423549
12.3%
57546
 
3.9%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
57546
 
3.9%
423549
12.3%
354606
28.5%
233031
17.2%
114089
 
7.4%
01918
 
1.0%

KBA05_HERST4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.781775164
Minimum0
Maximum9
Zeros3323
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:14.692498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.251338051
Coefficient of variation (CV)0.4498343603
Kurtosis3.093951437
Mean2.781775164
Median Absolute Deviation (MAD)1
Skewness0.6926824009
Sum377409
Variance1.565846918
MonotonicityNot monotonic
2021-12-24T13:28:14.774961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
349414
25.8%
234231
17.9%
423303
12.2%
115811
 
8.2%
58657
 
4.5%
03323
 
1.7%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
03323
 
1.7%
115811
 
8.2%
234231
17.9%
349414
25.8%
423303
12.2%
58657
 
4.5%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
58657
 
4.5%
423303
12.2%
349414
25.8%
234231
17.9%
115811
 
8.2%
03323
 
1.7%

KBA05_HERST5
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.637286986
Minimum0
Maximum9
Zeros6536
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:14.873502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.281040486
Coefficient of variation (CV)0.4857417842
Kurtosis3.097299781
Mean2.637286986
Median Absolute Deviation (MAD)1
Skewness0.6075345583
Sum357806
Variance1.641064727
MonotonicityNot monotonic
2021-12-24T13:28:14.955938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
347060
24.6%
236128
18.9%
422702
11.8%
116600
 
8.7%
06536
 
3.4%
55713
 
3.0%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
06536
 
3.4%
116600
 
8.7%
236128
18.9%
347060
24.6%
422702
11.8%
55713
 
3.0%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
55713
 
3.0%
422702
11.8%
347060
24.6%
236128
18.9%
116600
 
8.7%
06536
 
3.4%

KBA05_HERSTTEMP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean2.635900512
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:15.056510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.345302872
Coefficient of variation (CV)0.5103769531
Kurtosis2.926074855
Mean2.635900512
Median Absolute Deviation (MAD)1
Skewness1.10300346
Sum373573
Variance1.809839817
MonotonicityNot monotonic
2021-12-24T13:28:15.157043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
239565
20.6%
336916
19.3%
131375
16.4%
422189
11.6%
510389
 
5.4%
91291
 
0.7%
(Missing)49927
26.1%
ValueCountFrequency (%)
131375
16.4%
239565
20.6%
336916
19.3%
422189
11.6%
510389
 
5.4%
91291
 
0.7%
ValueCountFrequency (%)
91291
 
0.7%
510389
 
5.4%
422189
11.6%
336916
19.3%
239565
20.6%
131375
16.4%

KBA05_KRSAQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.432882245
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:15.267609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.146147698
Coefficient of variation (CV)0.3338732924
Kurtosis2.703972222
Mean3.432882245
Median Absolute Deviation (MAD)1
Skewness0.5289774311
Sum465746
Variance1.313654545
MonotonicityNot monotonic
2021-12-24T13:28:15.348049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
351784
27.0%
437551
19.6%
522505
11.7%
216369
 
8.5%
16530
 
3.4%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
16530
 
3.4%
216369
 
8.5%
351784
27.0%
437551
19.6%
522505
11.7%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
522505
11.7%
437551
19.6%
351784
27.0%
216369
 
8.5%
16530
 
3.4%

KBA05_KRSHERST1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.153568901
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:15.468729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.102287526
Coefficient of variation (CV)0.3495365284
Kurtosis4.176923227
Mean3.153568901
Median Absolute Deviation (MAD)1
Skewness0.8807288914
Sum427851
Variance1.215037789
MonotonicityNot monotonic
2021-12-24T13:28:15.559233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
354162
28.3%
435643
18.6%
226974
14.1%
510622
 
5.5%
17338
 
3.8%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
17338
 
3.8%
226974
14.1%
354162
28.3%
435643
18.6%
510622
 
5.5%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
510622
 
5.5%
435643
18.6%
354162
28.3%
226974
14.1%
17338
 
3.8%

KBA05_KRSHERST2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.131596792
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:15.679920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.128022417
Coefficient of variation (CV)0.3602067863
Kurtosis3.820571144
Mean3.131596792
Median Absolute Deviation (MAD)1
Skewness0.839196906
Sum424870
Variance1.272434572
MonotonicityNot monotonic
2021-12-24T13:28:15.780485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
356249
29.3%
431873
16.6%
225649
13.4%
511992
 
6.3%
18976
 
4.7%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
18976
 
4.7%
225649
13.4%
356249
29.3%
431873
16.6%
511992
 
6.3%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
511992
 
6.3%
431873
16.6%
356249
29.3%
225649
13.4%
18976
 
4.7%

KBA05_KRSHERST3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.94104163
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:15.901169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.164364988
Coefficient of variation (CV)0.3959022465
Kurtosis3.768932513
Mean2.94104163
Median Absolute Deviation (MAD)1
Skewness0.9626158155
Sum399017
Variance1.355745826
MonotonicityNot monotonic
2021-12-24T13:28:15.991592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
354705
28.5%
232629
17.0%
423810
12.4%
112992
 
6.8%
510603
 
5.5%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
112992
 
6.8%
232629
17.0%
354705
28.5%
423810
12.4%
510603
 
5.5%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
510603
 
5.5%
423810
12.4%
354705
28.5%
232629
17.0%
112992
 
6.8%

KBA05_KRSKLEIN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
2.0
85741 
1.0
26881 
3.0
22117 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.085741
44.7%
1.026881
 
14.0%
3.022117
 
11.5%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:16.102160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:16.192624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.085741
63.2%
1.026881
 
19.8%
3.022117
 
16.3%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSOBER
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
2.0
85619 
3.0
28364 
1.0
20756 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.085619
44.7%
3.028364
 
14.8%
1.020756
 
10.8%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:16.311256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:16.393703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.085619
63.1%
3.028364
 
20.9%
1.020756
 
15.3%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSVAN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
2.0
89816 
3.0
23317 
1.0
21606 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.089816
46.9%
3.023317
 
12.2%
1.021606
 
11.3%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:16.504547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:16.584989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.089816
66.2%
3.023317
 
17.2%
1.021606
 
15.9%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSZUL
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
2.0
75450 
3.0
32866 
1.0
26423 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.075450
39.4%
3.032866
17.1%
1.026423
 
13.8%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:16.675454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:16.755872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.075450
55.6%
3.032866
24.2%
1.026423
 
19.5%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KW1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.764638245
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:16.866456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.145709034
Coefficient of variation (CV)0.4144155339
Kurtosis4.646652796
Mean2.764638245
Median Absolute Deviation (MAD)1
Skewness1.076300717
Sum375084
Variance1.312649191
MonotonicityNot monotonic
2021-12-24T13:28:16.967000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
352512
27.4%
236262
18.9%
422702
11.8%
117624
 
9.2%
55639
 
2.9%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
117624
 
9.2%
236262
18.9%
352512
27.4%
422702
11.8%
55639
 
2.9%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
55639
 
2.9%
422702
11.8%
352512
27.4%
236262
18.9%
117624
 
9.2%

KBA05_KW2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.117489239
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:17.097816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.084034674
Coefficient of variation (CV)0.3477268375
Kurtosis4.769373248
Mean3.117489239
Median Absolute Deviation (MAD)1
Skewness1.021033737
Sum422956
Variance1.175131174
MonotonicityNot monotonic
2021-12-24T13:28:17.198472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
360732
31.7%
429722
15.5%
226382
13.8%
510702
 
5.6%
17201
 
3.8%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
17201
 
3.8%
226382
13.8%
360732
31.7%
429722
15.5%
510702
 
5.6%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
510702
 
5.6%
429722
15.5%
360732
31.7%
226382
13.8%
17201
 
3.8%

KBA05_KW3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean1.820692553
Minimum0
Maximum9
Zeros20709
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:17.307444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.373208414
Coefficient of variation (CV)0.7542231182
Kurtosis3.530311822
Mean1.820692553
Median Absolute Deviation (MAD)1
Skewness1.155774056
Sum247017
Variance1.885701349
MonotonicityNot monotonic
2021-12-24T13:28:17.412991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
143421
22.7%
233749
17.6%
020709
 
10.8%
319739
 
10.3%
417121
 
8.9%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
020709
10.8%
143421
22.7%
233749
17.6%
319739
10.3%
417121
 
8.9%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
417121
 
8.9%
319739
10.3%
233749
17.6%
143421
22.7%
020709
10.8%

KBA05_MAXAH
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.801086444
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:17.550801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.269736377
Coefficient of variation (CV)0.3340456462
Kurtosis0.4178195758
Mean3.801086444
Median Absolute Deviation (MAD)1
Skewness0.07048457009
Sum515701
Variance1.612230468
MonotonicityNot monotonic
2021-12-24T13:28:17.635416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
554394
28.4%
333702
17.6%
222176
 
11.6%
421803
 
11.4%
12664
 
1.4%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
12664
 
1.4%
222176
11.6%
333702
17.6%
421803
11.4%
554394
28.4%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
554394
28.4%
421803
11.4%
333702
17.6%
222176
11.6%
12664
 
1.4%

KBA05_MAXBJ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
4.0
43353 
1.0
36367 
2.0
30395 
3.0
24624 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.043353
22.6%
1.036367
19.0%
2.030395
15.9%
3.024624
12.8%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:17.751273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:17.835931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.043353
32.0%
1.036367
26.8%
2.030395
22.4%
3.024624
18.1%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MAXHERST
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.538998467
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:17.936129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.175723947
Coefficient of variation (CV)0.4630660325
Kurtosis4.991627253
Mean2.538998467
Median Absolute Deviation (MAD)1
Skewness1.473583122
Sum344471
Variance1.382326799
MonotonicityNot monotonic
2021-12-24T13:28:18.406079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
257631
30.1%
333420
17.4%
120339
 
10.6%
416532
 
8.6%
56817
 
3.6%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
120339
 
10.6%
257631
30.1%
333420
17.4%
416532
 
8.6%
56817
 
3.6%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
56817
 
3.6%
416532
 
8.6%
333420
17.4%
257631
30.1%
120339
 
10.6%

KBA05_MAXSEG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
2.0
52499 
1.0
33893 
3.0
31548 
4.0
16799 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.052499
27.4%
1.033893
17.7%
3.031548
16.5%
4.016799
 
8.8%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:18.606833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:18.703523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.052499
38.7%
1.033893
25.0%
3.031548
23.3%
4.016799
 
12.4%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MAXVORB
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
2.0
65054 
1.0
45305 
3.0
24380 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row3.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.065054
33.9%
1.045305
23.6%
3.024380
 
12.7%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:18.831421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:18.922177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.065054
47.9%
1.045305
33.4%
3.024380
 
18.0%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MOD1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean1.681245946
Minimum0
Maximum9
Zeros32586
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:19.008945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.387115574
Coefficient of variation (CV)0.8250521448
Kurtosis3.671424746
Mean1.681245946
Median Absolute Deviation (MAD)1
Skewness1.103421243
Sum228098
Variance1.924089615
MonotonicityNot monotonic
2021-12-24T13:28:19.099704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
238783
20.2%
032586
17.0%
129887
15.6%
321684
 
11.3%
411799
 
6.2%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
032586
17.0%
129887
15.6%
238783
20.2%
321684
11.3%
411799
 
6.2%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
411799
 
6.2%
321684
11.3%
238783
20.2%
129887
15.6%
032586
17.0%

KBA05_MOD2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.044172711
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:19.214394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.078154428
Coefficient of variation (CV)0.3541699276
Kurtosis5.167665521
Mean3.044172711
Median Absolute Deviation (MAD)1
Skewness1.050445188
Sum413009
Variance1.162416972
MonotonicityNot monotonic
2021-12-24T13:28:19.317118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
359631
31.1%
429940
15.6%
228743
15.0%
18413
 
4.4%
58012
 
4.2%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
18413
 
4.4%
228743
15.0%
359631
31.1%
429940
15.6%
58012
 
4.2%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
58012
 
4.2%
429940
15.6%
359631
31.1%
228743
15.0%
18413
 
4.4%

KBA05_MOD3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.076766024
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:19.428315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.117152643
Coefficient of variation (CV)0.363093142
Kurtosis4.12444309
Mean3.076766024
Median Absolute Deviation (MAD)1
Skewness0.9233859669
Sum417431
Variance1.248030027
MonotonicityNot monotonic
2021-12-24T13:28:19.520069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
353055
27.7%
432880
17.2%
230521
15.9%
59756
 
5.1%
18527
 
4.4%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
18527
 
4.4%
230521
15.9%
353055
27.7%
432880
17.2%
59756
 
5.1%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
59756
 
5.1%
432880
17.2%
353055
27.7%
230521
15.9%
18527
 
4.4%

KBA05_MOD4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.759272363
Minimum0
Maximum9
Zeros5107
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:19.626783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.360023721
Coefficient of variation (CV)0.4928921623
Kurtosis1.861920435
Mean2.759272363
Median Absolute Deviation (MAD)1
Skewness0.5176912715
Sum374356
Variance1.849664522
MonotonicityNot monotonic
2021-12-24T13:28:19.710559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
343414
22.7%
231214
16.3%
423991
12.5%
119435
 
10.1%
511578
 
6.0%
05107
 
2.7%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
05107
 
2.7%
119435
10.1%
231214
16.3%
343414
22.7%
423991
12.5%
511578
 
6.0%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
511578
 
6.0%
423991
12.5%
343414
22.7%
231214
16.3%
119435
10.1%
05107
 
2.7%

KBA05_MOD8
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
1.0
45515 
2.0
45005 
0.0
26798 
3.0
17421 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.045515
23.7%
2.045005
23.5%
0.026798
14.0%
3.017421
 
9.1%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:19.865863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:19.990060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.045515
33.5%
2.045005
33.2%
0.026798
19.8%
3.017421
 
12.8%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MODTEMP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean2.912831187
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:20.081117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.24516257
Coefficient of variation (CV)0.4274750199
Kurtosis-0.8770609878
Mean2.912831187
Median Absolute Deviation (MAD)1
Skewness-0.1790883695
Sum412821
Variance1.550429825
MonotonicityNot monotonic
2021-12-24T13:28:20.174694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
345649
23.8%
439322
20.5%
129149
15.2%
216451
 
8.6%
510389
 
5.4%
6765
 
0.4%
(Missing)49927
26.1%
ValueCountFrequency (%)
129149
15.2%
216451
 
8.6%
345649
23.8%
439322
20.5%
510389
 
5.4%
6765
 
0.4%
ValueCountFrequency (%)
6765
 
0.4%
510389
 
5.4%
439322
20.5%
345649
23.8%
216451
 
8.6%
129149
15.2%

KBA05_MOTOR
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
3.0
58357 
2.0
32087 
4.0
29964 
1.0
14331 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row2.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.058357
30.4%
2.032087
16.7%
4.029964
15.6%
1.014331
 
7.5%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:20.312782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:20.406653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.058357
43.0%
2.032087
23.7%
4.029964
22.1%
1.014331
 
10.6%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MOTRAD
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
1.0
73299 
0.0
26539 
3.0
18115 
2.0
16842 
9.0
 
877

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.073299
38.2%
0.026539
 
13.8%
3.018115
 
9.5%
2.016842
 
8.8%
9.0877
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:20.551747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:20.654471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.073299
54.0%
0.026539
 
19.6%
3.018115
 
13.4%
2.016842
 
12.4%
9.0877
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
1.0
55086 
0.0
36867 
2.0
34669 
3.0
8117 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.055086
28.7%
0.036867
19.2%
2.034669
18.1%
3.08117
 
4.2%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:20.772156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:20.869925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.055086
40.6%
0.036867
27.2%
2.034669
25.6%
3.08117
 
6.0%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG10
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.162701221
Minimum0
Maximum9
Zeros9635
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:20.980623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.193768704
Coefficient of variation (CV)0.551980409
Kurtosis5.95690075
Mean2.162701221
Median Absolute Deviation (MAD)1
Skewness1.14716283
Sum293418
Variance1.425083719
MonotonicityNot monotonic
2021-12-24T13:28:21.069697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
253413
27.9%
332759
17.1%
125270
13.2%
413662
 
7.1%
09635
 
5.0%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
09635
 
5.0%
125270
13.2%
253413
27.9%
332759
17.1%
413662
 
7.1%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
413662
 
7.1%
332759
17.1%
253413
27.9%
125270
13.2%
09635
 
5.0%

KBA05_SEG2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.921184917
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:21.171425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.090809521
Coefficient of variation (CV)0.3734133759
Kurtosis5.347360956
Mean2.921184917
Median Absolute Deviation (MAD)1
Skewness1.076245236
Sum396323
Variance1.189865412
MonotonicityNot monotonic
2021-12-24T13:28:21.271594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
358986
30.8%
231540
16.5%
426569
13.9%
111652
 
6.1%
55992
 
3.1%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
111652
 
6.1%
231540
16.5%
358986
30.8%
426569
13.9%
55992
 
3.1%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
55992
 
3.1%
426569
13.9%
358986
30.8%
231540
16.5%
111652
 
6.1%

KBA05_SEG3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.890264756
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:21.386286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.092004989
Coefficient of variation (CV)0.377821785
Kurtosis5.38632872
Mean2.890264756
Median Absolute Deviation (MAD)1
Skewness1.196567801
Sum392128
Variance1.192474897
MonotonicityNot monotonic
2021-12-24T13:28:21.491005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
354355
28.4%
238346
20.0%
425556
13.3%
110165
 
5.3%
56317
 
3.3%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
110165
 
5.3%
238346
20.0%
354355
28.4%
425556
13.3%
56317
 
3.3%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
56317
 
3.3%
425556
13.3%
354355
28.4%
238346
20.0%
110165
 
5.3%

KBA05_SEG4
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.049848163
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:21.622652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.068621911
Coefficient of variation (CV)0.3503852828
Kurtosis5.439691246
Mean3.049848163
Median Absolute Deviation (MAD)1
Skewness1.123933236
Sum413779
Variance1.141952789
MonotonicityNot monotonic
2021-12-24T13:28:21.726374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
363481
33.1%
227738
14.5%
426713
13.9%
58951
 
4.7%
17856
 
4.1%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
17856
 
4.1%
227738
14.5%
363481
33.1%
426713
13.9%
58951
 
4.7%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
58951
 
4.7%
426713
13.9%
363481
33.1%
227738
14.5%
17856
 
4.1%

KBA05_SEG5
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean1.784229613
Minimum0
Maximum9
Zeros18467
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:21.854031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.29501673
Coefficient of variation (CV)0.7258128221
Kurtosis5.052159987
Mean1.784229613
Median Absolute Deviation (MAD)1
Skewness1.340142636
Sum242070
Variance1.677068332
MonotonicityNot monotonic
2021-12-24T13:28:21.937866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
144430
23.2%
238419
20.0%
321287
 
11.1%
018467
 
9.6%
412136
 
6.3%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
018467
9.6%
144430
23.2%
238419
20.0%
321287
11.1%
412136
 
6.3%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
412136
 
6.3%
321287
11.1%
238419
20.0%
144430
23.2%
018467
9.6%

KBA05_SEG6
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
0.0
111010 
1.0
23729 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0111010
57.9%
1.023729
 
12.4%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:22.058582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:22.138022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0111010
81.8%
1.023729
 
17.5%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG7
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
0.0
53730 
1.0
43092 
2.0
29327 
3.0
8590 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.053730
28.0%
1.043092
22.5%
2.029327
15.3%
3.08590
 
4.5%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:22.228504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:22.315316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.053730
39.6%
1.043092
31.8%
2.029327
21.6%
3.08590
 
6.3%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG8
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
0.0
54785 
1.0
42095 
2.0
28374 
3.0
9485 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.054785
28.6%
1.042095
22.0%
2.028374
14.8%
3.09485
 
4.9%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:22.440979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:22.528744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.054785
40.4%
1.042095
31.0%
2.028374
20.9%
3.09485
 
7.0%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG9
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Memory size1.5 MiB
1.0
51705 
2.0
38399 
0.0
34324 
3.0
10311 
9.0
 
933

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.051705
27.0%
2.038399
20.0%
0.034324
17.9%
3.010311
 
5.4%
9.0933
 
0.5%
(Missing)55980
29.2%

Length

2021-12-24T13:28:22.640575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:22.736661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.051705
38.1%
2.038399
28.3%
0.034324
25.3%
3.010311
 
7.6%
9.0933
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_VORB0
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.305074002
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:22.841416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.15624528
Coefficient of variation (CV)0.3498394528
Kurtosis2.801743753
Mean3.305074002
Median Absolute Deviation (MAD)1
Skewness0.5879478751
Sum448406
Variance1.336903148
MonotonicityNot monotonic
2021-12-24T13:28:22.943280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
346855
24.4%
440443
21.1%
223263
12.1%
516742
 
8.7%
17436
 
3.9%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
17436
 
3.9%
223263
12.1%
346855
24.4%
440443
21.1%
516742
 
8.7%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
516742
 
8.7%
440443
21.1%
346855
24.4%
223263
12.1%
17436
 
3.9%

KBA05_VORB1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.025930185
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:23.064996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.101034329
Coefficient of variation (CV)0.3638664019
Kurtosis4.755839511
Mean3.025930185
Median Absolute Deviation (MAD)1
Skewness1.023447839
Sum410534
Variance1.212276593
MonotonicityNot monotonic
2021-12-24T13:28:23.202626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
360285
31.5%
228627
14.9%
426987
14.1%
19530
 
5.0%
59310
 
4.9%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
19530
 
5.0%
228627
14.9%
360285
31.5%
426987
14.1%
59310
 
4.9%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
59310
 
4.9%
426987
14.1%
360285
31.5%
228627
14.9%
19530
 
5.0%

KBA05_VORB2
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.306968276
Minimum0
Maximum9
Zeros11641
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:23.345244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.345094423
Coefficient of variation (CV)0.5830571824
Kurtosis2.980059335
Mean2.306968276
Median Absolute Deviation (MAD)1
Skewness0.7745051864
Sum312991
Variance1.809279007
MonotonicityNot monotonic
2021-12-24T13:28:23.469911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
341123
21.5%
237903
19.8%
125242
13.2%
413973
 
7.3%
011641
 
6.1%
54857
 
2.5%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
011641
 
6.1%
125242
13.2%
237903
19.8%
341123
21.5%
413973
 
7.3%
54857
 
2.5%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
54857
 
2.5%
413973
 
7.3%
341123
21.5%
237903
19.8%
125242
13.2%
011641
 
6.1%

KBA05_ZUL1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.785217289
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:23.588591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.076782703
Coefficient of variation (CV)0.3866063545
Kurtosis6.259647492
Mean2.785217289
Median Absolute Deviation (MAD)1
Skewness1.216591826
Sum377876
Variance1.159460989
MonotonicityNot monotonic
2021-12-24T13:28:23.693310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
358874
30.7%
235712
18.6%
422116
 
11.5%
114304
 
7.5%
53733
 
1.9%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
114304
 
7.5%
235712
18.6%
358874
30.7%
422116
 
11.5%
53733
 
1.9%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
53733
 
1.9%
422116
 
11.5%
358874
30.7%
235712
18.6%
114304
 
7.5%

KBA05_ZUL2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.088161153
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:23.809000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.086680768
Coefficient of variation (CV)0.3518860298
Kurtosis4.76578942
Mean3.088161153
Median Absolute Deviation (MAD)1
Skewness1.030775698
Sum418977
Variance1.180875091
MonotonicityNot monotonic
2021-12-24T13:28:23.939650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
357828
30.2%
430838
16.1%
229151
15.2%
59630
 
5.0%
17292
 
3.8%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
17292
 
3.8%
229151
15.2%
357828
30.2%
430838
16.1%
59630
 
5.0%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
59630
 
5.0%
430838
16.1%
357828
30.2%
229151
15.2%
17292
 
3.8%

KBA05_ZUL3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.969124064
Minimum0
Maximum9
Zeros5137
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:24.090246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.306666695
Coefficient of variation (CV)0.4400849097
Kurtosis2.204287452
Mean2.969124064
Median Absolute Deviation (MAD)1
Skewness0.2948038427
Sum402827
Variance1.707377853
MonotonicityNot monotonic
2021-12-24T13:28:24.195962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
345482
23.7%
433681
17.6%
227829
14.5%
111362
 
5.9%
511248
 
5.9%
05137
 
2.7%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
05137
 
2.7%
111362
 
5.9%
227829
14.5%
345482
23.7%
433681
17.6%
511248
 
5.9%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
511248
 
5.9%
433681
17.6%
345482
23.7%
227829
14.5%
111362
 
5.9%
05137
 
2.7%

KBA05_ZUL4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean2.45306327
Minimum0
Maximum9
Zeros8776
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:24.311653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.470194643
Coefficient of variation (CV)0.5993300952
Kurtosis1.32264108
Mean2.45306327
Median Absolute Deviation (MAD)1
Skewness0.6872581693
Sum332812
Variance2.161472289
MonotonicityNot monotonic
2021-12-24T13:28:24.450279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
236207
18.9%
130214
15.8%
326900
14.0%
422123
 
11.5%
510519
 
5.5%
08776
 
4.6%
9933
 
0.5%
(Missing)55980
29.2%
ValueCountFrequency (%)
08776
 
4.6%
130214
15.8%
236207
18.9%
326900
14.0%
422123
11.5%
510519
 
5.5%
9933
 
0.5%
ValueCountFrequency (%)
9933
 
0.5%
510519
 
5.5%
422123
11.5%
326900
14.0%
236207
18.9%
130214
15.8%
08776
 
4.6%

KBA13_ALTERHALTER_30
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
62692 
2.0
37652 
1.0
19842 
4.0
15586 
5.0
 
4599

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.062692
32.7%
2.037652
19.6%
1.019842
 
10.4%
4.015586
 
8.1%
5.04599
 
2.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:24.625809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:24.746485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.062692
44.7%
2.037652
26.8%
1.019842
 
14.1%
4.015586
 
11.1%
5.04599
 
3.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_45
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
57839 
4.0
30023 
2.0
28553 
1.0
12198 
5.0
11758 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row4.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.057839
30.2%
4.030023
15.7%
2.028553
14.9%
1.012198
 
6.4%
5.011758
 
6.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:24.870673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:24.944587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.057839
41.2%
4.030023
21.4%
2.028553
20.3%
1.012198
 
8.7%
5.011758
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
59346 
2.0
32838 
4.0
26116 
1.0
11949 
5.0
10122 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.059346
31.0%
2.032838
17.1%
4.026116
13.6%
1.011949
 
6.2%
5.010122
 
5.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:25.064447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:25.149063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.059346
42.3%
2.032838
23.4%
4.026116
18.6%
1.011949
 
8.5%
5.010122
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_61
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
57867 
4.0
34652 
2.0
22348 
5.0
19208 
1.0
6296 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.057867
30.2%
4.034652
18.1%
2.022348
 
11.7%
5.019208
 
10.0%
1.06296
 
3.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:25.265869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:25.337515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.057867
41.2%
4.034652
24.7%
2.022348
 
15.9%
5.019208
 
13.7%
1.06296
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
2.0
57412 
3.0
49273 
1.0
19181 
4.0
13973 
0.0
 
532

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.057412
30.0%
3.049273
25.7%
1.019181
 
10.0%
4.013973
 
7.3%
0.0532
 
0.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:25.454233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:25.520066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.057412
40.9%
3.049273
35.1%
1.019181
 
13.7%
4.013973
 
10.0%
0.0532
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64824 
2.0
39530 
4.0
25948 
1.0
9128 
0.0
 
941

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.064824
33.8%
2.039530
20.6%
4.025948
13.5%
1.09128
 
4.8%
0.0941
 
0.5%
(Missing)51281
26.8%

Length

2021-12-24T13:28:25.662594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:25.753045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064824
46.2%
2.039530
28.2%
4.025948
18.5%
1.09128
 
6.5%
0.0941
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
49583 
2.0
47973 
0.0
25498 
3.0
17317 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row0.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.049583
25.9%
2.047973
25.0%
0.025498
13.3%
3.017317
 
9.0%
(Missing)51281
26.8%

Length

2021-12-24T13:28:25.865671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:25.965726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.049583
35.3%
2.047973
34.2%
0.025498
18.2%
3.017317
 
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
0.0
78976 
1.0
49804 
2.0
11591 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.078976
41.2%
1.049804
26.0%
2.011591
 
6.0%
(Missing)51281
26.8%

Length

2021-12-24T13:28:26.056439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:26.136879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.078976
56.3%
1.049804
35.5%
2.011591
 
8.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANZAHL_PKW
Real number (ℝ≥0)

MISSING

Distinct1250
Distinct (%)0.9%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean667.2312158
Minimum5
Maximum2300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:26.247741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile247
Q1430
median593
Q3828
95-th percentile1400
Maximum2300
Range2295
Interquartile range (IQR)398

Descriptive statistics

Standard deviation340.4817224
Coefficient of variation (CV)0.5102904575
Kurtosis1.749850177
Mean667.2312158
Median Absolute Deviation (MAD)189
Skewness1.204304691
Sum93659913
Variance115927.8033
MonotonicityNot monotonic
2021-12-24T13:28:26.398450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14002489
 
1.3%
15001718
 
0.9%
13001413
 
0.7%
16001264
 
0.7%
1700796
 
0.4%
1800599
 
0.3%
1900332
 
0.2%
523302
 
0.2%
464294
 
0.2%
552283
 
0.1%
Other values (1240)130881
68.3%
(Missing)51281
 
26.8%
ValueCountFrequency (%)
52
< 0.1%
62
< 0.1%
81
 
< 0.1%
102
< 0.1%
111
 
< 0.1%
124
< 0.1%
131
 
< 0.1%
143
< 0.1%
151
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
230083
 
< 0.1%
220057
 
< 0.1%
2100120
 
0.1%
2000254
 
0.1%
1900332
 
0.2%
1800599
 
0.3%
1700796
 
0.4%
16001264
0.7%
15001718
0.9%
14002489
1.3%

KBA13_AUDI
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
62598 
4.0
34838 
2.0
22560 
5.0
14801 
1.0
 
5574

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.062598
32.7%
4.034838
18.2%
2.022560
 
11.8%
5.014801
 
7.7%
1.05574
 
2.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:26.563465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:26.653754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.062598
44.6%
4.034838
24.8%
2.022560
 
16.1%
5.014801
 
10.5%
1.05574
 
4.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_AUTOQUOTE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
65977 
2.0
28420 
4.0
27543 
1.0
9342 
5.0
9089 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.065977
34.4%
2.028420
14.8%
4.027543
14.4%
1.09342
 
4.9%
5.09089
 
4.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:26.779046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:26.882001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.065977
47.0%
2.028420
20.2%
4.027543
19.6%
1.09342
 
6.7%
5.09089
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BAUMAX
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
103960 
2.0
13328 
5.0
12105 
3.0
 
6198
4.0
 
4780

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0103960
54.2%
2.013328
 
7.0%
5.012105
 
6.3%
3.06198
 
3.2%
4.04780
 
2.5%
(Missing)51281
26.8%

Length

2021-12-24T13:28:27.024559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:27.113321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0103960
74.1%
2.013328
 
9.5%
5.012105
 
8.6%
3.06198
 
4.4%
4.04780
 
3.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_1999
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64537 
2.0
36663 
4.0
21288 
1.0
12481 
5.0
 
5402

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.064537
33.7%
2.036663
19.1%
4.021288
 
11.1%
1.012481
 
6.5%
5.05402
 
2.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:27.225494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:27.326188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064537
46.0%
2.036663
26.1%
4.021288
 
15.2%
1.012481
 
8.9%
5.05402
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2000
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
62846 
2.0
37646 
4.0
19646 
1.0
15585 
5.0
 
4648

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.062846
32.8%
2.037646
19.6%
4.019646
 
10.3%
1.015585
 
8.1%
5.04648
 
2.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:27.427873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:27.525291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.062846
44.8%
2.037646
26.8%
4.019646
 
14.0%
1.015585
 
11.1%
5.04648
 
3.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2004
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
67399 
4.0
29396 
2.0
28320 
5.0
8220 
1.0
7036 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.067399
35.2%
4.029396
15.3%
2.028320
14.8%
5.08220
 
4.3%
1.07036
 
3.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:27.639653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:27.739893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.067399
48.0%
4.029396
20.9%
2.028320
20.2%
5.08220
 
5.9%
1.07036
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2006
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66954 
4.0
33312 
2.0
25019 
5.0
9690 
1.0
 
5396

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row4.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.066954
34.9%
4.033312
17.4%
2.025019
 
13.1%
5.09690
 
5.1%
1.05396
 
2.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:27.846639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:27.937435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066954
47.7%
4.033312
23.7%
2.025019
 
17.8%
5.09690
 
6.9%
1.05396
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2008
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.698805309
Minimum0
Maximum5
Zeros19625
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:28.023320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.474305083
Coefficient of variation (CV)0.5462806368
Kurtosis-0.5096319439
Mean2.698805309
Median Absolute Deviation (MAD)1
Skewness-0.3776022473
Sum378834
Variance2.173575477
MonotonicityNot monotonic
2021-12-24T13:28:28.147458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
352663
27.5%
224206
12.6%
019625
 
10.2%
419500
 
10.2%
517514
 
9.1%
16863
 
3.6%
(Missing)51281
26.8%
ValueCountFrequency (%)
019625
 
10.2%
16863
 
3.6%
224206
12.6%
352663
27.5%
419500
 
10.2%
517514
 
9.1%
ValueCountFrequency (%)
517514
 
9.1%
419500
 
10.2%
352663
27.5%
224206
12.6%
16863
 
3.6%
019625
 
10.2%

KBA13_BJ_2009
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.694046491
Minimum0
Maximum5
Zeros16351
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:28.247696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.469412712
Coefficient of variation (CV)0.5454296043
Kurtosis-0.6337266346
Mean2.694046491
Median Absolute Deviation (MAD)1
Skewness-0.3194372109
Sum378166
Variance2.159173717
MonotonicityNot monotonic
2021-12-24T13:28:28.338881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
355093
28.7%
419010
 
9.9%
517598
 
9.2%
216538
 
8.6%
016351
 
8.5%
115781
 
8.2%
(Missing)51281
26.8%
ValueCountFrequency (%)
016351
 
8.5%
115781
 
8.2%
216538
 
8.6%
355093
28.7%
419010
 
9.9%
517598
 
9.2%
ValueCountFrequency (%)
517598
 
9.2%
419010
 
9.9%
355093
28.7%
216538
 
8.6%
115781
 
8.2%
016351
 
8.5%

KBA13_BMW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
59844 
4.0
40287 
5.0
22682 
2.0
14829 
1.0
 
2729

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.059844
31.2%
4.040287
21.0%
5.022682
 
11.8%
2.014829
 
7.7%
1.02729
 
1.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:28.464873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:28.547931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.059844
42.6%
4.040287
28.7%
5.022682
 
16.2%
2.014829
 
10.6%
1.02729
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_0_1400
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.12568123
Minimum0
Maximum5
Zeros28511
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:28.670310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.392846325
Coefficient of variation (CV)0.6552470358
Kurtosis-0.7052963514
Mean2.12568123
Median Absolute Deviation (MAD)1
Skewness-0.08564161896
Sum298384
Variance1.940020886
MonotonicityNot monotonic
2021-12-24T13:28:28.760790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
344967
23.5%
237090
19.4%
028511
14.9%
112155
 
6.3%
411092
 
5.8%
56556
 
3.4%
(Missing)51281
26.8%
ValueCountFrequency (%)
028511
14.9%
112155
 
6.3%
237090
19.4%
344967
23.5%
411092
 
5.8%
56556
 
3.4%
ValueCountFrequency (%)
56556
 
3.4%
411092
 
5.8%
344967
23.5%
237090
19.4%
112155
 
6.3%
028511
14.9%

KBA13_CCM_1000
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.248626853
Minimum0
Maximum5
Zeros19982
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:28.859297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.389220869
Coefficient of variation (CV)0.6178085379
Kurtosis-0.76400977
Mean2.248626853
Median Absolute Deviation (MAD)1
Skewness-0.05594406889
Sum315642
Variance1.929934622
MonotonicityNot monotonic
2021-12-24T13:28:28.961775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
351534
26.9%
125761
13.4%
222578
11.8%
019982
 
10.4%
412457
 
6.5%
58059
 
4.2%
(Missing)51281
26.8%
ValueCountFrequency (%)
019982
 
10.4%
125761
13.4%
222578
11.8%
351534
26.9%
412457
 
6.5%
58059
 
4.2%
ValueCountFrequency (%)
58059
 
4.2%
412457
 
6.5%
351534
26.9%
222578
11.8%
125761
13.4%
019982
 
10.4%

KBA13_CCM_1200
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.147701448
Minimum0
Maximum5
Zeros29378
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:29.050714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.429985286
Coefficient of variation (CV)0.6658212606
Kurtosis-0.8115685174
Mean2.147701448
Median Absolute Deviation (MAD)1
Skewness-0.09900420865
Sum301475
Variance2.044857917
MonotonicityNot monotonic
2021-12-24T13:28:29.141222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
347369
24.7%
231275
16.3%
029378
15.3%
113226
 
6.9%
412023
 
6.3%
57100
 
3.7%
(Missing)51281
26.8%
ValueCountFrequency (%)
029378
15.3%
113226
 
6.9%
231275
16.3%
347369
24.7%
412023
 
6.3%
57100
 
3.7%
ValueCountFrequency (%)
57100
 
3.7%
412023
 
6.3%
347369
24.7%
231275
16.3%
113226
 
6.9%
029378
15.3%

KBA13_CCM_1400
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64535 
2.0
35211 
4.0
24398 
1.0
9923 
5.0
 
6304

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.064535
33.7%
2.035211
18.4%
4.024398
 
12.7%
1.09923
 
5.2%
5.06304
 
3.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:29.243795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:29.332222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064535
46.0%
2.035211
25.1%
4.024398
 
17.4%
1.09923
 
7.1%
5.06304
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1401_2500
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66677 
4.0
29280 
2.0
29267 
1.0
9730 
5.0
 
5417

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.066677
34.8%
4.029280
15.3%
2.029267
15.3%
1.09730
 
5.1%
5.05417
 
2.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:29.904651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:29.995137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066677
47.5%
4.029280
20.9%
2.029267
20.8%
1.09730
 
6.9%
5.05417
 
3.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1500
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
51263 
4.0
36375 
3.0
27491 
2.0
14493 
5.0
10749 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row3.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.051263
26.7%
4.036375
19.0%
3.027491
14.3%
2.014493
 
7.6%
5.010749
 
5.6%
(Missing)51281
26.8%

Length

2021-12-24T13:28:30.121860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:30.217781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.051263
36.5%
4.036375
25.9%
3.027491
19.6%
2.014493
 
10.3%
5.010749
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1600
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66226 
2.0
33449 
4.0
26082 
1.0
7474 
5.0
7140 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.066226
34.6%
2.033449
17.5%
4.026082
 
13.6%
1.07474
 
3.9%
5.07140
 
3.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:30.335695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:30.431646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066226
47.2%
2.033449
23.8%
4.026082
 
18.6%
1.07474
 
5.3%
5.07140
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1800
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.364590977
Minimum0
Maximum5
Zeros24878
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:30.538177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.428590317
Coefficient of variation (CV)0.6041595907
Kurtosis-0.6169242322
Mean2.364590977
Median Absolute Deviation (MAD)1
Skewness-0.2320613574
Sum331920
Variance2.040870293
MonotonicityNot monotonic
2021-12-24T13:28:30.661843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
350532
26.4%
232244
16.8%
024878
13.0%
414621
 
7.6%
59814
 
5.1%
18282
 
4.3%
(Missing)51281
26.8%
ValueCountFrequency (%)
024878
13.0%
18282
 
4.3%
232244
16.8%
350532
26.4%
414621
 
7.6%
59814
 
5.1%
ValueCountFrequency (%)
59814
 
5.1%
414621
 
7.6%
350532
26.4%
232244
16.8%
18282
 
4.3%
024878
13.0%

KBA13_CCM_2000
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64936 
4.0
36307 
2.0
22433 
5.0
12922 
1.0
 
3773

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.064936
33.9%
4.036307
18.9%
2.022433
 
11.7%
5.012922
 
6.7%
1.03773
 
2.0%
(Missing)51281
26.8%

Length

2021-12-24T13:28:30.794276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:30.877865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064936
46.3%
4.036307
25.9%
2.022433
 
16.0%
5.012922
 
9.2%
1.03773
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_2500
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.693177366
Minimum0
Maximum5
Zeros15780
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:30.980102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.44436976
Coefficient of variation (CV)0.5363069578
Kurtosis-0.5604463394
Mean2.693177366
Median Absolute Deviation (MAD)1
Skewness-0.3046412394
Sum378044
Variance2.086204003
MonotonicityNot monotonic
2021-12-24T13:28:31.099518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
353790
28.1%
220948
 
10.9%
418775
 
9.8%
517150
 
8.9%
015780
 
8.2%
113928
 
7.3%
(Missing)51281
26.8%
ValueCountFrequency (%)
015780
 
8.2%
113928
 
7.3%
220948
 
10.9%
353790
28.1%
418775
 
9.8%
517150
 
8.9%
ValueCountFrequency (%)
517150
 
8.9%
418775
 
9.8%
353790
28.1%
220948
 
10.9%
113928
 
7.3%
015780
 
8.2%

KBA13_CCM_2501
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.778608117
Minimum0
Maximum5
Zeros14392
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:31.202242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.454250695
Coefficient of variation (CV)0.5233738022
Kurtosis-0.5527724243
Mean2.778608117
Median Absolute Deviation (MAD)1
Skewness-0.328632819
Sum390036
Variance2.114845084
MonotonicityNot monotonic
2021-12-24T13:28:31.310951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
354189
28.3%
520064
 
10.5%
418887
 
9.9%
218762
 
9.8%
014392
 
7.5%
114077
 
7.3%
(Missing)51281
26.8%
ValueCountFrequency (%)
014392
 
7.5%
114077
 
7.3%
218762
 
9.8%
354189
28.3%
418887
 
9.9%
520064
 
10.5%
ValueCountFrequency (%)
520064
 
10.5%
418887
 
9.9%
354189
28.3%
218762
 
9.8%
114077
 
7.3%
014392
 
7.5%

KBA13_CCM_3000
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.820589723
Minimum0
Maximum5
Zeros9117
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:31.398057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.370008524
Coefficient of variation (CV)0.4857170517
Kurtosis-0.479093909
Mean2.820589723
Median Absolute Deviation (MAD)1
Skewness-0.2613188946
Sum395929
Variance1.876923357
MonotonicityNot monotonic
2021-12-24T13:28:31.488850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
355545
29.0%
219577
 
10.2%
419392
 
10.1%
518958
 
9.9%
117782
 
9.3%
09117
 
4.8%
(Missing)51281
26.8%
ValueCountFrequency (%)
09117
 
4.8%
117782
 
9.3%
219577
 
10.2%
355545
29.0%
419392
 
10.1%
518958
 
9.9%
ValueCountFrequency (%)
518958
 
9.9%
419392
 
10.1%
355545
29.0%
219577
 
10.2%
117782
 
9.3%
09117
 
4.8%

KBA13_CCM_3001
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
49506 
4.0
41514 
3.0
29546 
5.0
19776 
2.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.049506
25.8%
4.041514
21.7%
3.029546
15.4%
5.019776
 
10.3%
2.029
 
< 0.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:31.599471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:31.680311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.049506
35.3%
4.041514
29.6%
3.029546
21.0%
5.019776
 
14.1%
2.029
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FAB_ASIEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63667 
2.0
37645 
4.0
19556 
1.0
14236 
5.0
 
5267

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.063667
33.2%
2.037645
19.6%
4.019556
 
10.2%
1.014236
 
7.4%
5.05267
 
2.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:31.781113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:31.869765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063667
45.4%
2.037645
26.8%
4.019556
 
13.9%
1.014236
 
10.1%
5.05267
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FAB_SONSTIGE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
65793 
2.0
35331 
4.0
20848 
1.0
12481 
5.0
 
5918

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.065793
34.3%
2.035331
18.4%
4.020848
 
10.9%
1.012481
 
6.5%
5.05918
 
3.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:31.972371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:32.052782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.065793
46.9%
2.035331
25.2%
4.020848
 
14.9%
1.012481
 
8.9%
5.05918
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FIAT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63246 
4.0
34555 
2.0
22605 
5.0
15511 
1.0
 
4454

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.063246
33.0%
4.034555
18.0%
2.022605
 
11.8%
5.015511
 
8.1%
1.04454
 
2.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:32.171556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:32.262028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063246
45.1%
4.034555
24.6%
2.022605
 
16.1%
5.015511
 
11.1%
1.04454
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FORD
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
56900 
2.0
33925 
4.0
22627 
1.0
15656 
5.0
11263 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.056900
29.7%
2.033925
17.7%
4.022627
 
11.8%
1.015656
 
8.2%
5.011263
 
5.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:32.387473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:32.495060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.056900
40.5%
2.033925
24.2%
4.022627
 
16.1%
1.015656
 
11.2%
5.011263
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
50677 
4.0
39632 
5.0
35612 
2.0
11177 
1.0
 
3273

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row5.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.050677
26.4%
4.039632
20.7%
5.035612
18.6%
2.011177
 
5.8%
1.03273
 
1.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:32.609684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:32.699153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.050677
36.1%
4.039632
28.2%
5.035612
25.4%
2.011177
 
8.0%
1.03273
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_20
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64441 
2.0
38657 
4.0
20211 
1.0
11972 
5.0
 
5090

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row4.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.064441
33.6%
2.038657
20.2%
4.020211
 
10.5%
1.011972
 
6.2%
5.05090
 
2.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:32.813845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:32.901609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064441
45.9%
2.038657
27.5%
4.020211
 
14.4%
1.011972
 
8.5%
5.05090
 
3.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_25
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64228 
2.0
38768 
1.0
18686 
4.0
15205 
5.0
 
3484

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row4.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.064228
33.5%
2.038768
20.2%
1.018686
 
9.7%
4.015205
 
7.9%
5.03484
 
1.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:33.021075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:33.112452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064228
45.8%
2.038768
27.6%
1.018686
 
13.3%
4.015205
 
10.8%
5.03484
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_30
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61714 
2.0
35576 
4.0
18573 
1.0
16786 
5.0
7722 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.061714
32.2%
2.035576
18.6%
4.018573
 
9.7%
1.016786
 
8.8%
5.07722
 
4.0%
(Missing)51281
26.8%

Length

2021-12-24T13:28:33.224489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:33.316243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061714
44.0%
2.035576
25.3%
4.018573
 
13.2%
1.016786
 
12.0%
5.07722
 
5.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_35
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
56851 
2.0
31050 
4.0
26476 
1.0
14700 
5.0
11294 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row4.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.056851
29.7%
2.031050
16.2%
4.026476
13.8%
1.014700
 
7.7%
5.011294
 
5.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:33.431932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:33.523686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.056851
40.5%
2.031050
22.1%
4.026476
18.9%
1.014700
 
10.5%
5.011294
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_40
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60310 
4.0
29175 
2.0
28437 
5.0
12012 
1.0
10437 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row4.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.060310
31.5%
4.029175
15.2%
2.028437
14.8%
5.012012
 
6.3%
1.010437
 
5.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:33.654336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:33.766144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060310
43.0%
4.029175
20.8%
2.028437
20.3%
5.012012
 
8.6%
1.010437
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_45
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60843 
4.0
30884 
2.0
26972 
5.0
13018 
1.0
8654 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.060843
31.7%
4.030884
16.1%
2.026972
14.1%
5.013018
 
6.8%
1.08654
 
4.5%
(Missing)51281
26.8%

Length

2021-12-24T13:28:33.906189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:33.989963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060843
43.3%
4.030884
22.0%
2.026972
19.2%
5.013018
 
9.3%
1.08654
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_50
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
59247 
2.0
32773 
4.0
25887 
1.0
12945 
5.0
9519 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.059247
30.9%
2.032773
17.1%
4.025887
13.5%
1.012945
 
6.8%
5.09519
 
5.0%
(Missing)51281
26.8%

Length

2021-12-24T13:28:34.113632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:34.209375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.059247
42.2%
2.032773
23.3%
4.025887
18.4%
1.012945
 
9.2%
5.09519
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_55
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
59544 
2.0
30682 
4.0
27427 
5.0
11597 
1.0
11121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.059544
31.1%
2.030682
16.0%
4.027427
14.3%
5.011597
 
6.1%
1.011121
 
5.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:34.316369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:34.406799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.059544
42.4%
2.030682
21.9%
4.027427
19.5%
5.011597
 
8.3%
1.011121
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60412 
2.0
30056 
4.0
27691 
5.0
11178 
1.0
11034 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.060412
31.5%
2.030056
15.7%
4.027691
14.4%
5.011178
 
5.8%
1.011034
 
5.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:34.507329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:34.607965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060412
43.0%
2.030056
21.4%
4.027691
19.7%
5.011178
 
8.0%
1.011034
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_65
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61924 
4.0
37290 
2.0
18789 
5.0
18355 
1.0
 
4013

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.061924
32.3%
4.037290
19.5%
2.018789
 
9.8%
5.018355
 
9.6%
1.04013
 
2.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:34.739445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:34.846076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061924
44.1%
4.037290
26.6%
2.018789
 
13.4%
5.018355
 
13.1%
1.04013
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_66
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
57128 
4.0
33143 
2.0
23948 
5.0
18698 
1.0
7454 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.057128
29.8%
4.033143
17.3%
2.023948
12.5%
5.018698
 
9.8%
1.07454
 
3.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:34.961427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:35.061560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.057128
40.7%
4.033143
23.6%
2.023948
17.1%
5.018698
 
13.3%
1.07454
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_ASIEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63321 
2.0
36659 
4.0
20876 
1.0
14233 
5.0
 
5282

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row4.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.063321
33.0%
2.036659
19.1%
4.020876
 
10.9%
1.014233
 
7.4%
5.05282
 
2.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:35.175161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:35.280108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063321
45.1%
2.036659
26.1%
4.020876
 
14.9%
1.014233
 
10.1%
5.05282
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_AUDI_VW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64373 
4.0
30416 
2.0
26364 
5.0
10545 
1.0
8673 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row1.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.064373
33.6%
4.030416
15.9%
2.026364
13.8%
5.010545
 
5.5%
1.08673
 
4.5%
(Missing)51281
26.8%

Length

2021-12-24T13:28:35.412396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:35.511443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064373
45.9%
4.030416
21.7%
2.026364
18.8%
5.010545
 
7.5%
1.08673
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_BMW_BENZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60281 
4.0
41075 
5.0
23372 
2.0
13040 
1.0
 
2603

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.060281
31.5%
4.041075
21.4%
5.023372
 
12.2%
2.013040
 
6.8%
1.02603
 
1.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:35.627142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:35.709920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060281
42.9%
4.041075
29.3%
5.023372
 
16.7%
2.013040
 
9.3%
1.02603
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_EUROPA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60179 
4.0
28723 
2.0
28551 
5.0
13154 
1.0
9764 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row5.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.060179
31.4%
4.028723
15.0%
2.028551
14.9%
5.013154
 
6.9%
1.09764
 
5.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:35.822823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:35.913227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060179
42.9%
4.028723
20.5%
2.028551
20.3%
5.013154
 
9.4%
1.09764
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_FORD_OPEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
54418 
2.0
33320 
4.0
23537 
1.0
18666 
5.0
10430 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.054418
28.4%
2.033320
17.4%
4.023537
12.3%
1.018666
 
9.7%
5.010430
 
5.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:36.023977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:36.114420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.054418
38.8%
2.033320
23.7%
4.023537
16.8%
1.018666
 
13.3%
5.010430
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_SONST
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
65793 
2.0
35331 
4.0
20848 
1.0
12481 
5.0
 
5918

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.065793
34.3%
2.035331
18.4%
4.020848
 
10.9%
1.012481
 
6.5%
5.05918
 
3.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:36.214983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:36.295472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.065793
46.9%
2.035331
25.2%
4.020848
 
14.9%
1.012481
 
8.9%
5.05918
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HHZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60206 
4.0
38221 
5.0
29535 
2.0
11108 
1.0
 
1301

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row3.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.060206
31.4%
4.038221
19.9%
5.029535
15.4%
2.011108
 
5.8%
1.01301
 
0.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:36.399187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:36.476472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060206
42.9%
4.038221
27.2%
5.029535
21.0%
2.011108
 
7.9%
1.01301
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_0_140
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.188699945
Minimum0
Maximum5
Zeros21541
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:36.577053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.524134908
Coefficient of variation (CV)0.6963653979
Kurtosis-1.148299836
Mean2.188699945
Median Absolute Deviation (MAD)2
Skewness0.09855194772
Sum307230
Variance2.322987218
MonotonicityNot monotonic
2021-12-24T13:28:36.667478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
349931
26.1%
141680
21.7%
021541
11.2%
415328
 
8.0%
510221
 
5.3%
21670
 
0.9%
(Missing)51281
26.8%
ValueCountFrequency (%)
021541
11.2%
141680
21.7%
21670
 
0.9%
349931
26.1%
415328
 
8.0%
510221
 
5.3%
ValueCountFrequency (%)
510221
 
5.3%
415328
 
8.0%
349931
26.1%
21670
 
0.9%
141680
21.7%
021541
11.2%

KBA13_KMH_110
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
114895 
3.0
14232 
2.0
 
11244

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0114895
59.9%
3.014232
 
7.4%
2.011244
 
5.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:36.775998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:36.848423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0114895
81.9%
3.014232
 
10.1%
2.011244
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_140
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
43684 
4.0
35044 
3.0
29866 
2.0
20659 
5.0
11118 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.043684
22.8%
4.035044
18.3%
3.029866
15.6%
2.020659
10.8%
5.011118
 
5.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:36.945839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:37.037272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.043684
31.1%
4.035044
25.0%
3.029866
21.3%
2.020659
14.7%
5.011118
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_140_210
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61808 
2.0
36066 
4.0
18826 
1.0
18796 
5.0
 
4875

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.061808
32.3%
2.036066
18.8%
4.018826
 
9.8%
1.018796
 
9.8%
5.04875
 
2.5%
(Missing)51281
26.8%

Length

2021-12-24T13:28:37.149893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:37.240341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061808
44.0%
2.036066
25.7%
4.018826
 
13.4%
1.018796
 
13.4%
5.04875
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_180
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61785 
2.0
36007 
4.0
21193 
1.0
16451 
5.0
 
4935

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.061785
32.2%
2.036007
18.8%
4.021193
 
11.1%
1.016451
 
8.6%
5.04935
 
2.6%
(Missing)51281
26.8%

Length

2021-12-24T13:28:37.348833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:37.429222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061785
44.0%
2.036007
25.7%
4.021193
 
15.1%
1.016451
 
11.7%
5.04935
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_210
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66165 
4.0
33351 
2.0
24506 
5.0
10808 
1.0
 
5541

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.066165
34.5%
4.033351
17.4%
2.024506
 
12.8%
5.010808
 
5.6%
1.05541
 
2.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:37.541431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:37.631912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066165
47.1%
4.033351
23.8%
2.024506
 
17.5%
5.010808
 
7.7%
1.05541
 
3.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_211
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.745481617
Minimum0
Maximum5
Zeros20007
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:37.730393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.510259898
Coefficient of variation (CV)0.5500892407
Kurtosis-0.5625299899
Mean2.745481617
Median Absolute Deviation (MAD)1
Skewness-0.3802347973
Sum385386
Variance2.280884959
MonotonicityNot monotonic
2021-12-24T13:28:37.830927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
351770
27.0%
222926
12.0%
520539
 
10.7%
020007
 
10.4%
418800
 
9.8%
16329
 
3.3%
(Missing)51281
26.8%
ValueCountFrequency (%)
020007
 
10.4%
16329
 
3.3%
222926
12.0%
351770
27.0%
418800
 
9.8%
520539
 
10.7%
ValueCountFrequency (%)
520539
 
10.7%
418800
 
9.8%
351770
27.0%
222926
12.0%
16329
 
3.3%
020007
 
10.4%

KBA13_KMH_250
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.739119904
Minimum0
Maximum5
Zeros20125
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:37.923368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.509620005
Coefficient of variation (CV)0.551133232
Kurtosis-0.5625984589
Mean2.739119904
Median Absolute Deviation (MAD)1
Skewness-0.3786828138
Sum384493
Variance2.27895256
MonotonicityNot monotonic
2021-12-24T13:28:38.021129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
352063
27.2%
222839
11.9%
520348
 
10.6%
020125
 
10.5%
418630
 
9.7%
16366
 
3.3%
(Missing)51281
26.8%
ValueCountFrequency (%)
020125
 
10.5%
16366
 
3.3%
222839
11.9%
352063
27.2%
418630
 
9.7%
520348
 
10.6%
ValueCountFrequency (%)
520348
 
10.6%
418630
 
9.7%
352063
27.2%
222839
11.9%
16366
 
3.3%
020125
 
10.5%

KBA13_KMH_251
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
115323 
3.0
22793 
2.0
 
2255

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0115323
60.2%
3.022793
 
11.9%
2.02255
 
1.2%
(Missing)51281
26.8%

Length

2021-12-24T13:28:38.134568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:38.215036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0115323
82.2%
3.022793
 
16.2%
2.02255
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSAQUOT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
62447 
2.0
29461 
4.0
28066 
5.0
10422 
1.0
9975 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.062447
32.6%
2.029461
15.4%
4.028066
14.6%
5.010422
 
5.4%
1.09975
 
5.2%
(Missing)51281
26.8%

Length

2021-12-24T13:28:38.305521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:38.395998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.062447
44.5%
2.029461
21.0%
4.028066
20.0%
5.010422
 
7.4%
1.09975
 
7.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSHERST_AUDI_VW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61316 
4.0
33418 
2.0
26417 
5.0
10918 
1.0
8302 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.061316
32.0%
4.033418
17.4%
2.026417
13.8%
5.010918
 
5.7%
1.08302
 
4.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:38.510658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:38.586937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061316
43.7%
4.033418
23.8%
2.026417
18.8%
5.010918
 
7.8%
1.08302
 
5.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSHERST_BMW_BENZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64110 
4.0
34919 
2.0
21198 
5.0
16056 
1.0
 
4088

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row5.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.064110
33.5%
4.034919
18.2%
2.021198
 
11.1%
5.016056
 
8.4%
1.04088
 
2.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:38.695432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:38.777868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064110
45.7%
4.034919
24.9%
2.021198
 
15.1%
5.016056
 
11.4%
1.04088
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSHERST_FORD_OPEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
56960 
2.0
32947 
4.0
24435 
1.0
16969 
5.0
9060 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.056960
29.7%
2.032947
17.2%
4.024435
12.7%
1.016969
 
8.9%
5.09060
 
4.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:38.916735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:39.009274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.056960
40.6%
2.032947
23.5%
4.024435
17.4%
1.016969
 
12.1%
5.09060
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSSEG_KLEIN
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
2.0
129376 
1.0
 
7132
3.0
 
3863

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0129376
67.5%
1.07132
 
3.7%
3.03863
 
2.0%
(Missing)51281
 
26.8%

Length

2021-12-24T13:28:39.119708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:39.200092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0129376
92.2%
1.07132
 
5.1%
3.03863
 
2.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSSEG_OBER
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
2.0
99651 
3.0
22142 
1.0
18551 
0.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.099651
52.0%
3.022142
 
11.6%
1.018551
 
9.7%
0.027
 
< 0.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:39.290506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:39.380929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.099651
71.0%
3.022142
 
15.8%
1.018551
 
13.2%
0.027
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSSEG_VAN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
2.0
91432 
1.0
25726 
3.0
23153 
0.0
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.091432
47.7%
1.025726
 
13.4%
3.023153
 
12.1%
0.060
 
< 0.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:39.481281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:39.575129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.091432
65.1%
1.025726
 
18.3%
3.023153
 
16.5%
0.060
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSZUL_NEU
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
2.0
71560 
3.0
35741 
1.0
30394 
0.0
 
2676

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.071560
37.3%
3.035741
18.6%
1.030394
15.9%
0.02676
 
1.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:39.670506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:39.752908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.071560
51.0%
3.035741
25.5%
1.030394
21.7%
0.02676
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_0_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63265 
2.0
35992 
4.0
21619 
1.0
14308 
5.0
 
5187

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.063265
33.0%
2.035992
18.8%
4.021619
 
11.3%
1.014308
 
7.5%
5.05187
 
2.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:39.843336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:39.933919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063265
45.1%
2.035992
25.6%
4.021619
 
15.4%
1.014308
 
10.2%
5.05187
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_110
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.60707696
Minimum0
Maximum5
Zeros19148
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:40.034464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.425965787
Coefficient of variation (CV)0.5469596061
Kurtosis-0.4586967915
Mean2.60707696
Median Absolute Deviation (MAD)1
Skewness-0.332508155
Sum365958
Variance2.033378427
MonotonicityNot monotonic
2021-12-24T13:28:40.142921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
353853
28.1%
227043
14.1%
019148
 
10.0%
417862
 
9.3%
514100
 
7.4%
18365
 
4.4%
(Missing)51281
26.8%
ValueCountFrequency (%)
019148
 
10.0%
18365
 
4.4%
227043
14.1%
353853
28.1%
417862
 
9.3%
514100
 
7.4%
ValueCountFrequency (%)
514100
 
7.4%
417862
 
9.3%
353853
28.1%
227043
14.1%
18365
 
4.4%
019148
 
10.0%

KBA13_KW_120
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.508488221
Minimum0
Maximum5
Zeros17157
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:40.233397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.554176205
Coefficient of variation (CV)0.619566874
Kurtosis-1.075043311
Mean2.508488221
Median Absolute Deviation (MAD)1
Skewness-0.1121705976
Sum352119
Variance2.415463677
MonotonicityNot monotonic
2021-12-24T13:28:40.315788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
350679
26.4%
132251
16.8%
418997
 
9.9%
017157
 
9.0%
516423
 
8.6%
24864
 
2.5%
(Missing)51281
26.8%
ValueCountFrequency (%)
017157
 
9.0%
132251
16.8%
24864
 
2.5%
350679
26.4%
418997
 
9.9%
516423
 
8.6%
ValueCountFrequency (%)
516423
 
8.6%
418997
 
9.9%
350679
26.4%
24864
 
2.5%
132251
16.8%
017157
 
9.0%

KBA13_KW_121
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.775751402
Minimum0
Maximum5
Zeros15218
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:40.416429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.474827616
Coefficient of variation (CV)0.5313255413
Kurtosis-0.600056625
Mean2.775751402
Median Absolute Deviation (MAD)1
Skewness-0.326024788
Sum389635
Variance2.175116497
MonotonicityNot monotonic
2021-12-24T13:28:40.505053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
352206
27.2%
520655
 
10.8%
219460
 
10.2%
419330
 
10.1%
015218
 
7.9%
113502
 
7.0%
(Missing)51281
26.8%
ValueCountFrequency (%)
015218
 
7.9%
113502
 
7.0%
219460
 
10.2%
352206
27.2%
419330
 
10.1%
520655
 
10.8%
ValueCountFrequency (%)
520655
 
10.8%
419330
 
10.1%
352206
27.2%
219460
 
10.2%
113502
 
7.0%
015218
 
7.9%

KBA13_KW_30
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
1.0
102600 
2.0
24871 
3.0
12900 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0102600
53.5%
2.024871
 
13.0%
3.012900
 
6.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:40.634038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:40.758602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0102600
73.1%
2.024871
 
17.7%
3.012900
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_40
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.190943998
Minimum0
Maximum5
Zeros19498
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:40.837017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.36030058
Coefficient of variation (CV)0.6208741898
Kurtosis-0.7316513023
Mean2.190943998
Median Absolute Deviation (MAD)1
Skewness-0.001832317544
Sum307545
Variance1.850417668
MonotonicityNot monotonic
2021-12-24T13:28:40.929510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
349006
25.6%
127705
14.5%
225440
13.3%
019498
 
10.2%
411668
 
6.1%
57054
 
3.7%
(Missing)51281
26.8%
ValueCountFrequency (%)
019498
 
10.2%
127705
14.5%
225440
13.3%
349006
25.6%
411668
 
6.1%
57054
 
3.7%
ValueCountFrequency (%)
57054
 
3.7%
411668
 
6.1%
349006
25.6%
225440
13.3%
127705
14.5%
019498
 
10.2%

KBA13_KW_50
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.170369948
Minimum0
Maximum5
Zeros29204
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:41.039997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.412521485
Coefficient of variation (CV)0.6508206061
Kurtosis-0.7098842924
Mean2.170369948
Median Absolute Deviation (MAD)1
Skewness-0.1319295587
Sum304657
Variance1.995216946
MonotonicityNot monotonic
2021-12-24T13:28:41.130494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
346474
24.2%
236567
19.1%
029204
15.2%
411741
 
6.1%
19197
 
4.8%
57188
 
3.8%
(Missing)51281
26.8%
ValueCountFrequency (%)
029204
15.2%
19197
 
4.8%
236567
19.1%
346474
24.2%
411741
 
6.1%
57188
 
3.8%
ValueCountFrequency (%)
57188
 
3.8%
411741
 
6.1%
346474
24.2%
236567
19.1%
19197
 
4.8%
029204
15.2%

KBA13_KW_60
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.165062584
Minimum0
Maximum5
Zeros24799
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:41.231005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.380791861
Coefficient of variation (CV)0.6377607149
Kurtosis-0.6772409111
Mean2.165062584
Median Absolute Deviation (MAD)1
Skewness-0.05280492599
Sum303912
Variance1.906586165
MonotonicityNot monotonic
2021-12-24T13:28:41.321486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
345022
23.5%
234854
18.2%
024799
12.9%
116890
 
8.8%
411782
 
6.1%
57024
 
3.7%
(Missing)51281
26.8%
ValueCountFrequency (%)
024799
12.9%
116890
 
8.8%
234854
18.2%
345022
23.5%
411782
 
6.1%
57024
 
3.7%
ValueCountFrequency (%)
57024
 
3.7%
411782
 
6.1%
345022
23.5%
234854
18.2%
116890
 
8.8%
024799
12.9%

KBA13_KW_61_120
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66849 
4.0
32120 
2.0
25705 
5.0
9223 
1.0
 
6474

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row4.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.066849
34.9%
4.032120
16.8%
2.025705
 
13.4%
5.09223
 
4.8%
1.06474
 
3.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:41.432087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:41.512547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066849
47.6%
4.032120
22.9%
2.025705
 
18.3%
5.09223
 
6.6%
1.06474
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_70
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.255230781
Minimum0
Maximum5
Zeros27083
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:41.603057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.415182554
Coefficient of variation (CV)0.627511191
Kurtosis-0.6695676825
Mean2.255230781
Median Absolute Deviation (MAD)1
Skewness-0.1849097869
Sum316569
Variance2.00274166
MonotonicityNot monotonic
2021-12-24T13:28:41.699866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
348311
25.2%
234903
18.2%
027083
14.1%
413320
 
7.0%
18805
 
4.6%
57949
 
4.1%
(Missing)51281
26.8%
ValueCountFrequency (%)
027083
14.1%
18805
 
4.6%
234903
18.2%
348311
25.2%
413320
 
7.0%
57949
 
4.1%
ValueCountFrequency (%)
57949
 
4.1%
413320
 
7.0%
348311
25.2%
234903
18.2%
18805
 
4.6%
027083
14.1%

KBA13_KW_80
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.269941797
Minimum0
Maximum5
Zeros23396
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:41.804325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.389211747
Coefficient of variation (CV)0.6120032456
Kurtosis-0.6372520249
Mean2.269941797
Median Absolute Deviation (MAD)1
Skewness-0.141427404
Sum318634
Variance1.929909279
MonotonicityNot monotonic
2021-12-24T13:28:41.904884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
348485
25.3%
233083
17.3%
023396
12.2%
114209
 
7.4%
413186
 
6.9%
58012
 
4.2%
(Missing)51281
26.8%
ValueCountFrequency (%)
023396
12.2%
114209
 
7.4%
233083
17.3%
348485
25.3%
413186
 
6.9%
58012
 
4.2%
ValueCountFrequency (%)
58012
 
4.2%
413186
 
6.9%
348485
25.3%
233083
17.3%
114209
 
7.4%
023396
12.2%

KBA13_KW_90
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.423349552
Minimum0
Maximum5
Zeros23538
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:42.005508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.429510243
Coefficient of variation (CV)0.5898902373
Kurtosis-0.5724649255
Mean2.423349552
Median Absolute Deviation (MAD)1
Skewness-0.2540340996
Sum340168
Variance2.043499533
MonotonicityNot monotonic
2021-12-24T13:28:42.104049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
350841
26.5%
231971
16.7%
023538
12.3%
415458
 
8.1%
510827
 
5.6%
17736
 
4.0%
(Missing)51281
26.8%
ValueCountFrequency (%)
023538
12.3%
17736
 
4.0%
231971
16.7%
350841
26.5%
415458
 
8.1%
510827
 
5.6%
ValueCountFrequency (%)
510827
 
5.6%
415458
 
8.1%
350841
26.5%
231971
16.7%
17736
 
4.0%
023538
12.3%

KBA13_MAZDA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
62795 
2.0
32851 
4.0
25662 
5.0
9629 
1.0
9434 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row5.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.062795
32.8%
2.032851
17.1%
4.025662
13.4%
5.09629
 
5.0%
1.09434
 
4.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:42.214005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:42.307182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.062795
44.7%
2.032851
23.4%
4.025662
18.3%
5.09629
 
6.9%
1.09434
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_MERCEDES
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
62517 
4.0
39555 
5.0
19915 
2.0
15485 
1.0
 
2899

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.062517
32.6%
4.039555
20.6%
5.019915
 
10.4%
2.015485
 
8.1%
1.02899
 
1.5%
(Missing)51281
26.8%

Length

2021-12-24T13:28:42.425759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:43.091666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.062517
44.5%
4.039555
28.2%
5.019915
 
14.2%
2.015485
 
11.0%
1.02899
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_MOTOR
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
84318 
2.0
25478 
4.0
18343 
1.0
12232 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.084318
44.0%
2.025478
 
13.3%
4.018343
 
9.6%
1.012232
 
6.4%
(Missing)51281
26.8%

Length

2021-12-24T13:28:43.192221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:43.281841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.084318
60.1%
2.025478
 
18.2%
4.018343
 
13.1%
1.012232
 
8.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_NISSAN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60497 
2.0
37341 
4.0
22362 
1.0
12752 
5.0
7419 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.060497
31.6%
2.037341
19.5%
4.022362
 
11.7%
1.012752
 
6.7%
5.07419
 
3.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:43.383250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:43.474049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060497
43.1%
2.037341
26.6%
4.022362
 
15.9%
1.012752
 
9.1%
5.07419
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_OPEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
54793 
2.0
32334 
4.0
24759 
1.0
17324 
5.0
11161 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.054793
28.6%
2.032334
16.9%
4.024759
12.9%
1.017324
 
9.0%
5.011161
 
5.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:43.604892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:43.693343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.054793
39.0%
2.032334
23.0%
4.024759
17.6%
1.017324
 
12.3%
5.011161
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_PEUGEOT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61079 
4.0
31281 
2.0
25895 
5.0
14268 
1.0
7848 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row5.0
3rd row5.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.061079
31.9%
4.031281
16.3%
2.025895
13.5%
5.014268
 
7.4%
1.07848
 
4.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:43.812984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:43.896423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061079
43.5%
4.031281
22.3%
2.025895
18.4%
5.014268
 
10.2%
1.07848
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_RENAULT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61452 
2.0
33417 
4.0
24131 
1.0
12427 
5.0
8944 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.061452
32.1%
2.033417
17.4%
4.024131
 
12.6%
1.012427
 
6.5%
5.08944
 
4.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:44.005029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:44.077528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061452
43.8%
2.033417
23.8%
4.024131
 
17.2%
1.012427
 
8.9%
5.08944
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_GELAENDEWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66469 
4.0
32294 
2.0
24139 
5.0
11782 
1.0
 
5687

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row5.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.066469
34.7%
4.032294
16.9%
2.024139
 
12.6%
5.011782
 
6.1%
1.05687
 
3.0%
(Missing)51281
26.8%

Length

2021-12-24T13:28:44.198395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:44.278756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066469
47.4%
4.032294
23.0%
2.024139
 
17.2%
5.011782
 
8.4%
1.05687
 
4.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_GROSSRAUMVANS
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
62379 
4.0
36893 
2.0
19278 
5.0
17324 
1.0
 
4497

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.062379
32.5%
4.036893
19.2%
2.019278
 
10.1%
5.017324
 
9.0%
1.04497
 
2.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:44.379192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:44.468252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.062379
44.4%
4.036893
26.3%
2.019278
 
13.7%
5.017324
 
12.3%
1.04497
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KLEINST
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60502 
2.0
33657 
4.0
23480 
1.0
15244 
5.0
7488 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row1.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.060502
31.6%
2.033657
17.6%
4.023480
 
12.3%
1.015244
 
8.0%
5.07488
 
3.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:44.575679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:44.664799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060502
43.1%
2.033657
24.0%
4.023480
 
16.7%
1.015244
 
10.9%
5.07488
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KLEINWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63083 
2.0
34770 
4.0
21568 
1.0
15567 
5.0
 
5383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row5.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.063083
32.9%
2.034770
18.1%
4.021568
 
11.3%
1.015567
 
8.1%
5.05383
 
2.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:44.780657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:44.887271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063083
44.9%
2.034770
24.8%
4.021568
 
15.4%
1.015567
 
11.1%
5.05383
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KOMPAKTKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66799 
2.0
36491 
4.0
18907 
1.0
13676 
5.0
 
4498

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.066799
34.9%
2.036491
19.0%
4.018907
 
9.9%
1.013676
 
7.1%
5.04498
 
2.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:44.996514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:45.096749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066799
47.6%
2.036491
26.0%
4.018907
 
13.5%
1.013676
 
9.7%
5.04498
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MINIVANS
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63756 
4.0
29945 
2.0
27254 
5.0
11895 
1.0
7521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.063756
33.3%
4.029945
15.6%
2.027254
14.2%
5.011895
 
6.2%
1.07521
 
3.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:45.196985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:45.281604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063756
45.4%
4.029945
21.3%
2.027254
19.4%
5.011895
 
8.5%
1.07521
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MINIWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
65642 
4.0
31445 
2.0
25553 
5.0
11404 
1.0
 
6327

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row5.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.065642
34.3%
4.031445
16.4%
2.025553
 
13.3%
5.011404
 
6.0%
1.06327
 
3.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:45.415862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:45.523608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.065642
46.8%
4.031445
22.4%
2.025553
 
18.2%
5.011404
 
8.1%
1.06327
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MITTELKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63519 
2.0
30709 
4.0
27356 
5.0
9570 
1.0
9217 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.063519
33.1%
2.030709
16.0%
4.027356
14.3%
5.09570
 
5.0%
1.09217
 
4.8%
(Missing)51281
26.8%

Length

2021-12-24T13:28:45.624136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:45.704535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063519
45.3%
2.030709
21.9%
4.027356
19.5%
5.09570
 
6.8%
1.09217
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_OBEREMITTELKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64136 
4.0
37215 
5.0
18275 
2.0
17590 
1.0
 
3155

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row2.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.064136
33.5%
4.037215
19.4%
5.018275
 
9.5%
2.017590
 
9.2%
1.03155
 
1.6%
(Missing)51281
26.8%

Length

2021-12-24T13:28:45.815088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:45.907669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064136
45.7%
4.037215
26.5%
5.018275
 
13.0%
2.017590
 
12.5%
1.03155
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_OBERKLASSE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.728291456
Minimum0
Maximum5
Zeros14998
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:46.005859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.474938551
Coefficient of variation (CV)0.5406088662
Kurtosis-0.6630675495
Mean2.728291456
Median Absolute Deviation (MAD)1
Skewness-0.3084380804
Sum382973
Variance2.175443728
MonotonicityNot monotonic
2021-12-24T13:28:46.106425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
355586
29.0%
519114
 
10.0%
418586
 
9.7%
117873
 
9.3%
014998
 
7.8%
214214
 
7.4%
(Missing)51281
26.8%
ValueCountFrequency (%)
014998
 
7.8%
117873
 
9.3%
214214
 
7.4%
355586
29.0%
418586
 
9.7%
519114
 
10.0%
ValueCountFrequency (%)
519114
 
10.0%
418586
 
9.7%
355586
29.0%
214214
 
7.4%
117873
 
9.3%
014998
 
7.8%

KBA13_SEG_SONSTIGE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66805 
2.0
34136 
4.0
25541 
5.0
7763 
1.0
 
6126

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row4.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.066805
34.9%
2.034136
17.8%
4.025541
 
13.3%
5.07763
 
4.1%
1.06126
 
3.2%
(Missing)51281
26.8%

Length

2021-12-24T13:28:46.216986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:46.297430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066805
47.6%
2.034136
24.3%
4.025541
 
18.2%
5.07763
 
5.5%
1.06126
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_SPORTWAGEN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.956465367
Minimum0
Maximum5
Zeros14430
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:46.387907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.445583832
Coefficient of variation (CV)0.4889567956
Kurtosis-0.2886516217
Mean2.956465367
Median Absolute Deviation (MAD)1
Skewness-0.5032481522
Sum415002
Variance2.089712616
MonotonicityNot monotonic
2021-12-24T13:28:46.517531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
354690
28.5%
523806
12.4%
422016
11.5%
218409
 
9.6%
014430
 
7.5%
17020
 
3.7%
(Missing)51281
26.8%
ValueCountFrequency (%)
014430
 
7.5%
17020
 
3.7%
218409
 
9.6%
354690
28.5%
422016
11.5%
523806
12.4%
ValueCountFrequency (%)
523806
12.4%
422016
11.5%
354690
28.5%
218409
 
9.6%
17020
 
3.7%
014430
 
7.5%

KBA13_SEG_UTILITIES
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
65610 
4.0
30533 
2.0
27502 
5.0
10414 
1.0
 
6312

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.065610
34.2%
4.030533
15.9%
2.027502
14.3%
5.010414
 
5.4%
1.06312
 
3.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:46.648565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:46.729185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.065610
46.7%
4.030533
21.8%
2.027502
19.6%
5.010414
 
7.4%
1.06312
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_VAN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63075 
4.0
34291 
2.0
22444 
5.0
14839 
1.0
 
5722

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.063075
32.9%
4.034291
17.9%
2.022444
 
11.7%
5.014839
 
7.7%
1.05722
 
3.0%
(Missing)51281
26.8%

Length

2021-12-24T13:28:46.849287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:46.929681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063075
44.9%
4.034291
24.4%
2.022444
 
16.0%
5.014839
 
10.6%
1.05722
 
4.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_WOHNMOBILE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.669404649
Minimum0
Maximum5
Zeros15839
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:47.019983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.404803512
Coefficient of variation (CV)0.5262609821
Kurtosis-0.4239869863
Mean2.669404649
Median Absolute Deviation (MAD)1
Skewness-0.3093967445
Sum374707
Variance1.973472908
MonotonicityNot monotonic
2021-12-24T13:28:47.120448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
354231
28.3%
225732
13.4%
418067
 
9.4%
015839
 
8.3%
515445
 
8.1%
111057
 
5.8%
(Missing)51281
26.8%
ValueCountFrequency (%)
015839
 
8.3%
111057
 
5.8%
225732
13.4%
354231
28.3%
418067
 
9.4%
515445
 
8.1%
ValueCountFrequency (%)
515445
 
8.1%
418067
 
9.4%
354231
28.3%
225732
13.4%
111057
 
5.8%
015839
 
8.3%

KBA13_SITZE_4
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61290 
4.0
40184 
5.0
21788 
2.0
14261 
1.0
 
2848

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.061290
32.0%
4.040184
21.0%
5.021788
 
11.4%
2.014261
 
7.4%
1.02848
 
1.5%
(Missing)51281
26.8%

Length

2021-12-24T13:28:47.239081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:47.321520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061290
43.7%
4.040184
28.6%
5.021788
 
15.5%
2.014261
 
10.2%
1.02848
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SITZE_5
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
59794 
2.0
40605 
1.0
23533 
4.0
13405 
5.0
 
3034

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.059794
31.2%
2.040605
21.2%
1.023533
 
12.3%
4.013405
 
7.0%
5.03034
 
1.6%
(Missing)51281
26.8%

Length

2021-12-24T13:28:47.432027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:47.514583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.059794
42.6%
2.040605
28.9%
1.023533
 
16.8%
4.013405
 
9.5%
5.03034
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SITZE_6
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
61775 
4.0
35900 
5.0
19235 
2.0
19023 
1.0
 
4438

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row3.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.061775
32.2%
4.035900
18.7%
5.019235
 
10.0%
2.019023
 
9.9%
1.04438
 
2.3%
(Missing)51281
26.8%

Length

2021-12-24T13:28:47.622859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:47.719390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061775
44.0%
4.035900
25.6%
5.019235
 
13.7%
2.019023
 
13.6%
1.04438
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_TOYOTA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
63780 
2.0
28473 
4.0
28238 
5.0
12014 
1.0
7866 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row5.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.063780
33.3%
2.028473
14.9%
4.028238
14.7%
5.012014
 
6.3%
1.07866
 
4.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:47.838986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:47.933425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.063780
45.4%
2.028473
20.3%
4.028238
20.1%
5.012014
 
8.6%
1.07866
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_0
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
60825 
4.0
38154 
5.0
19718 
2.0
18485 
1.0
 
3189

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.060825
31.7%
4.038154
19.9%
5.019718
 
10.3%
2.018485
 
9.6%
1.03189
 
1.7%
(Missing)51281
26.8%

Length

2021-12-24T13:28:48.051556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:48.140846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.060825
43.3%
4.038154
27.2%
5.019718
 
14.0%
2.018485
 
13.2%
1.03189
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
66631 
4.0
30553 
2.0
27597 
5.0
8710 
1.0
6880 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.066631
34.8%
4.030553
15.9%
2.027597
14.4%
5.08710
 
4.5%
1.06880
 
3.6%
(Missing)51281
26.8%

Length

2021-12-24T13:28:48.257458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:48.341234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.066631
47.5%
4.030553
21.8%
2.027597
19.7%
5.08710
 
6.2%
1.06880
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_1_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64227 
2.0
32132 
4.0
25130 
1.0
12854 
5.0
 
6028

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.064227
33.5%
2.032132
16.8%
4.025130
 
13.1%
1.012854
 
6.7%
5.06028
 
3.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:48.443959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:48.524315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064227
45.8%
2.032132
22.9%
4.025130
 
17.9%
1.012854
 
9.2%
5.06028
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
65598 
2.0
35863 
4.0
23148 
1.0
9815 
5.0
 
5947

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.065598
34.2%
2.035863
18.7%
4.023148
 
12.1%
1.09815
 
5.1%
5.05947
 
3.1%
(Missing)51281
26.8%

Length

2021-12-24T13:28:48.626925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:48.707292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.065598
46.7%
2.035863
25.5%
4.023148
 
16.5%
1.09815
 
7.0%
5.05947
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.011911292
Minimum0
Maximum5
Zeros31766
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:48.823407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.375629499
Coefficient of variation (CV)0.6837426202
Kurtosis-0.759494375
Mean2.011911292
Median Absolute Deviation (MAD)1
Skewness-0.0546854007
Sum282414
Variance1.892356517
MonotonicityNot monotonic
2021-12-24T13:28:48.997885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
343122
22.5%
238940
20.3%
031766
16.6%
112026
 
6.3%
49443
 
4.9%
55074
 
2.6%
(Missing)51281
26.8%
ValueCountFrequency (%)
031766
16.6%
112026
 
6.3%
238940
20.3%
343122
22.5%
49443
 
4.9%
55074
 
2.6%
ValueCountFrequency (%)
55074
 
2.6%
49443
 
4.9%
343122
22.5%
238940
20.3%
112026
 
6.3%
031766
16.6%

KBA13_VW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing51281
Missing (%)26.8%
Memory size1.5 MiB
3.0
64606 
4.0
28389 
2.0
28346 
5.0
9587 
1.0
9443 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row1.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.064606
33.7%
4.028389
14.8%
2.028346
14.8%
5.09587
 
5.0%
1.09443
 
4.9%
(Missing)51281
26.8%

Length

2021-12-24T13:28:49.113747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:49.213985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.064606
46.0%
4.028389
20.2%
2.028346
20.2%
5.09587
 
6.8%
1.09443
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KK_KUNDENTYP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing111937
Missing (%)58.4%
Infinite0
Infinite (%)0.0%
Mean3.421802672
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:49.314223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.623889984
Coefficient of variation (CV)0.4745714874
Kurtosis-1.149253485
Mean3.421802672
Median Absolute Deviation (MAD)1
Skewness0.1285611156
Sum272769
Variance2.637018679
MonotonicityNot monotonic
2021-12-24T13:28:49.414461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
317511
 
9.1%
215394
 
8.0%
512741
 
6.6%
411653
 
6.1%
611343
 
5.9%
111073
 
5.8%
(Missing)111937
58.4%
ValueCountFrequency (%)
111073
5.8%
215394
8.0%
317511
9.1%
411653
6.1%
512741
6.6%
611343
5.9%
ValueCountFrequency (%)
611343
5.9%
512741
6.6%
411653
6.1%
317511
9.1%
215394
8.0%
111073
5.8%

KKK
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing54260
Missing (%)28.3%
Memory size1.5 MiB
3.0
40739 
2.0
40049 
1.0
28850 
4.0
21950 
0.0
5804 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.040739
21.3%
2.040049
20.9%
1.028850
15.1%
4.021950
11.5%
0.05804
 
3.0%
(Missing)54260
28.3%

Length

2021-12-24T13:28:49.552153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:49.648878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.040739
29.7%
2.040049
29.1%
1.028850
21.0%
4.021950
16.0%
0.05804
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KOMBIALTER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
109179 
9
47051 
3
28344 
2
 
5561
1
 
1517

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4109179
57.0%
947051
24.6%
328344
 
14.8%
25561
 
2.9%
11517
 
0.8%

Length

2021-12-24T13:28:49.769504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:49.860484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4109179
57.0%
947051
24.6%
328344
 
14.8%
25561
 
2.9%
11517
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KONSUMNAEHE
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing46651
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean3.129978414
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:49.948956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.439739672
Coefficient of variation (CV)0.4599838981
Kurtosis-1.056228765
Mean3.129978414
Median Absolute Deviation (MAD)1
Skewness0.02010129011
Sum453850
Variance2.072850323
MonotonicityNot monotonic
2021-12-24T13:28:50.041519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
334383
17.9%
528405
14.8%
427995
14.6%
125886
13.5%
224857
13.0%
63229
 
1.7%
7246
 
0.1%
(Missing)46651
24.3%
ValueCountFrequency (%)
125886
13.5%
224857
13.0%
334383
17.9%
427995
14.6%
528405
14.8%
63229
 
1.7%
7246
 
0.1%
ValueCountFrequency (%)
7246
 
0.1%
63229
 
1.7%
528405
14.8%
427995
14.6%
334383
17.9%
224857
13.0%
125886
13.5%

KONSUMZELLE
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Memory size1.5 MiB
0.0
116619 
1.0
25106 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0116619
60.8%
1.025106
 
13.1%
(Missing)49927
26.1%

Length

2021-12-24T13:28:50.172256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:50.272785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0116619
82.3%
1.025106
 
17.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LP_FAMILIE_FEIN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean4.254448389
Minimum0
Maximum11
Zeros47369
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:50.351173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.492806628
Coefficient of variation (CV)1.056025651
Kurtosis-1.578131385
Mean4.254448389
Median Absolute Deviation (MAD)2
Skewness0.5245495186
Sum801704
Variance20.18531139
MonotonicityNot monotonic
2021-12-24T13:28:50.451693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
047369
24.7%
140769
21.3%
1036568
19.1%
228937
15.1%
1122289
11.6%
84686
 
2.4%
72960
 
1.5%
92428
 
1.3%
5903
 
0.5%
6831
 
0.4%
Other values (2)699
 
0.4%
(Missing)3213
 
1.7%
ValueCountFrequency (%)
047369
24.7%
140769
21.3%
228937
15.1%
3155
 
0.1%
4544
 
0.3%
5903
 
0.5%
6831
 
0.4%
72960
 
1.5%
84686
 
2.4%
92428
 
1.3%
ValueCountFrequency (%)
1122289
11.6%
1036568
19.1%
92428
 
1.3%
84686
 
2.4%
72960
 
1.5%
6831
 
0.4%
5903
 
0.5%
4544
 
0.3%
3155
 
0.1%
228937
15.1%

LP_FAMILIE_GROB
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean2.355043277
Minimum0
Maximum5
Zeros47369
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:50.544225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.052141644
Coefficient of variation (CV)0.8713817129
Kurtosis-1.606584382
Mean2.355043277
Median Absolute Deviation (MAD)2
Skewness0.2767634688
Sum443782
Variance4.211285328
MonotonicityNot monotonic
2021-12-24T13:28:50.644805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
561285
32.0%
047369
24.7%
140769
21.3%
228937
15.1%
48477
 
4.4%
31602
 
0.8%
(Missing)3213
 
1.7%
ValueCountFrequency (%)
047369
24.7%
140769
21.3%
228937
15.1%
31602
 
0.8%
48477
 
4.4%
561285
32.0%
ValueCountFrequency (%)
561285
32.0%
48477
 
4.4%
31602
 
0.8%
228937
15.1%
140769
21.3%
047369
24.7%

LP_LEBENSPHASE_FEIN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct41
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean18.18157069
Minimum0
Maximum40
Zeros47840
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:50.773475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q336
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)36

Descriptive statistics

Standard deviation15.00998521
Coefficient of variation (CV)0.8255604238
Kurtosis-1.479585124
Mean18.18157069
Median Absolute Deviation (MAD)16
Skewness0.1823497672
Sum3426117
Variance225.2996559
MonotonicityNot monotonic
2021-12-24T13:28:50.926439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
047840
25.0%
4018299
 
9.5%
2010851
 
5.7%
139972
 
5.2%
368821
 
4.6%
388648
 
4.5%
396785
 
3.5%
66646
 
3.5%
86297
 
3.3%
196143
 
3.2%
Other values (31)58137
30.3%
ValueCountFrequency (%)
047840
25.0%
1553
 
0.3%
2663
 
0.3%
3258
 
0.1%
4434
 
0.2%
52261
 
1.2%
66646
 
3.5%
71700
 
0.9%
86297
 
3.3%
92939
 
1.5%
ValueCountFrequency (%)
4018299
9.5%
396785
 
3.5%
388648
4.5%
374592
 
2.4%
368821
4.6%
351784
 
0.9%
34919
 
0.5%
33623
 
0.3%
325415
 
2.8%
313983
 
2.1%

LP_LEBENSPHASE_GROB
Real number (ℝ≥0)

MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean5.422693816
Minimum0
Maximum12
Zeros47728
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:51.057048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q312
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)12

Descriptive statistics

Standard deviation4.717907433
Coefficient of variation (CV)0.8700302088
Kurtosis-1.487400067
Mean5.422693816
Median Absolute Deviation (MAD)4
Skewness0.3181147429
Sum1021847
Variance22.25865055
MonotonicityNot monotonic
2021-12-24T13:28:51.175669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
047728
24.9%
1247145
24.6%
321739
11.3%
521069
11.0%
216904
 
8.8%
109398
 
4.9%
47851
 
4.1%
87280
 
3.8%
113326
 
1.7%
11908
 
1.0%
Other values (3)4091
 
2.1%
(Missing)3213
 
1.7%
ValueCountFrequency (%)
047728
24.9%
11908
 
1.0%
216904
 
8.8%
321739
11.3%
47851
 
4.1%
521069
11.0%
61602
 
0.8%
71197
 
0.6%
87280
 
3.8%
91292
 
0.7%
ValueCountFrequency (%)
1247145
24.6%
113326
 
1.7%
109398
 
4.9%
91292
 
0.7%
87280
 
3.8%
71197
 
0.6%
61602
 
0.8%
521069
11.0%
47851
 
4.1%
321739
11.3%

LP_STATUS_FEIN
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean6.687909615
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:51.308497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median7
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.090572785
Coefficient of variation (CV)0.46211342
Kurtosis-1.14949574
Mean6.687909615
Median Absolute Deviation (MAD)3
Skewness-0.4256118986
Sum1260263
Variance9.551640139
MonotonicityNot monotonic
2021-12-24T13:28:51.409039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1054653
28.5%
541912
21.9%
932916
17.2%
119271
 
10.1%
315364
 
8.0%
710574
 
5.5%
66502
 
3.4%
45004
 
2.6%
21404
 
0.7%
8839
 
0.4%
(Missing)3213
 
1.7%
ValueCountFrequency (%)
119271
 
10.1%
21404
 
0.7%
315364
 
8.0%
45004
 
2.6%
541912
21.9%
66502
 
3.4%
710574
 
5.5%
8839
 
0.4%
932916
17.2%
1054653
28.5%
ValueCountFrequency (%)
1054653
28.5%
932916
17.2%
8839
 
0.4%
710574
 
5.5%
66502
 
3.4%
541912
21.9%
45004
 
2.6%
315364
 
8.0%
21404
 
0.7%
119271
 
10.1%

LP_STATUS_GROB
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
2.0
62280 
5.0
54653 
4.0
33755 
1.0
20675 
3.0
17076 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row4.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.062280
32.5%
5.054653
28.5%
4.033755
17.6%
1.020675
 
10.8%
3.017076
 
8.9%
(Missing)3213
 
1.7%

Length

2021-12-24T13:28:51.529494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:51.607873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.062280
33.1%
5.054653
29.0%
4.033755
17.9%
1.020675
 
11.0%
3.017076
 
9.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MIN_GEBAEUDEJAHR
Real number (ℝ≥0)

MISSING

Distinct32
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean1993.056659
Minimum1985
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:51.750630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1992
Q11992
median1992
Q31992
95-th percentile1999
Maximum2016
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.080241078
Coefficient of variation (CV)0.001545485957
Kurtosis16.6545333
Mean1993.056659
Median Absolute Deviation (MAD)0
Skewness3.801723463
Sum282465955
Variance9.487885101
MonotonicityNot monotonic
2021-12-24T13:28:51.921580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1992106438
55.5%
199411714
 
6.1%
19934084
 
2.1%
19952896
 
1.5%
19962618
 
1.4%
19972524
 
1.3%
20001241
 
0.6%
19911121
 
0.6%
19901022
 
0.5%
2001973
 
0.5%
Other values (22)7094
 
3.7%
(Missing)49927
26.1%
ValueCountFrequency (%)
198516
 
< 0.1%
198638
 
< 0.1%
1987118
 
0.1%
1988245
 
0.1%
1989581
 
0.3%
19901022
 
0.5%
19911121
 
0.6%
1992106438
55.5%
19934084
 
2.1%
199411714
 
6.1%
ValueCountFrequency (%)
20164
 
< 0.1%
201588
 
< 0.1%
2014147
0.1%
2013188
0.1%
2012219
0.1%
2011248
0.1%
2010213
0.1%
2009297
0.2%
2008352
0.2%
2007365
0.2%

MOBI_RASTER
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean2.90036338
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:52.040198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.527411359
Coefficient of variation (CV)0.5266275839
Kurtosis-1.119048648
Mean2.90036338
Median Absolute Deviation (MAD)1
Skewness0.2284179168
Sum411054
Variance2.33298546
MonotonicityNot monotonic
2021-12-24T13:28:52.142777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
137688
19.7%
329878
15.6%
424729
12.9%
222543
11.8%
521592
11.3%
65295
 
2.8%
(Missing)49927
26.1%
ValueCountFrequency (%)
137688
19.7%
222543
11.8%
329878
15.6%
424729
12.9%
521592
11.3%
65295
 
2.8%
ValueCountFrequency (%)
65295
 
2.8%
521592
11.3%
424729
12.9%
329878
15.6%
222543
11.8%
137688
19.7%

MOBI_REGIO
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing55980
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean3.627424966
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:52.243265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.28244416
Coefficient of variation (CV)0.3535411958
Kurtosis-0.5995691426
Mean3.627424966
Median Absolute Deviation (MAD)1
Skewness-0.662550733
Sum492140
Variance1.644663024
MonotonicityNot monotonic
2021-12-24T13:28:52.343879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
542651
22.3%
439148
20.4%
327399
14.3%
113204
 
6.9%
213182
 
6.9%
688
 
< 0.1%
(Missing)55980
29.2%
ValueCountFrequency (%)
113204
 
6.9%
213182
 
6.9%
327399
14.3%
439148
20.4%
542651
22.3%
688
 
< 0.1%
ValueCountFrequency (%)
688
 
< 0.1%
542651
22.3%
439148
20.4%
327399
14.3%
213182
 
6.9%
113204
 
6.9%

NATIONALITAET_KZ
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
139027 
0
48750 
2
 
2422
3
 
1453

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1139027
72.5%
048750
 
25.4%
22422
 
1.3%
31453
 
0.8%

Length

2021-12-24T13:28:52.484661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:52.585203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1139027
72.5%
048750
 
25.4%
22422
 
1.3%
31453
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ONLINE_AFFINITAET
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean2.764326917
Minimum0
Maximum5
Zeros4110
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:52.665746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.266049511
Coefficient of variation (CV)0.4579955803
Kurtosis-0.7882827556
Mean2.764326917
Median Absolute Deviation (MAD)1
Skewness0.2320495767
Sum520907
Variance1.602881364
MonotonicityNot monotonic
2021-12-24T13:28:52.766373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
276440
39.9%
439104
20.4%
328791
 
15.0%
521311
 
11.1%
118683
 
9.7%
04110
 
2.1%
(Missing)3213
 
1.7%
ValueCountFrequency (%)
04110
 
2.1%
118683
 
9.7%
276440
39.9%
328791
 
15.0%
439104
20.4%
521311
 
11.1%
ValueCountFrequency (%)
521311
 
11.1%
439104
20.4%
328791
 
15.0%
276440
39.9%
118683
 
9.7%
04110
 
2.1%

ORTSGR_KLS9
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing50476
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean5.119517482
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:52.876948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.159183781
Coefficient of variation (CV)0.4217553292
Kurtosis-0.8126478698
Mean5.119517482
Median Absolute Deviation (MAD)2
Skewness0.08996363608
Sum722753
Variance4.662074599
MonotonicityNot monotonic
2021-12-24T13:28:52.977465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
528741
15.0%
423133
12.1%
719363
 
10.1%
316046
 
8.4%
613456
 
7.0%
212468
 
6.5%
911430
 
6.0%
811108
 
5.8%
15431
 
2.8%
(Missing)50476
26.3%
ValueCountFrequency (%)
15431
 
2.8%
212468
6.5%
316046
8.4%
423133
12.1%
528741
15.0%
613456
7.0%
719363
10.1%
811108
 
5.8%
911430
 
6.0%
ValueCountFrequency (%)
911430
 
6.0%
811108
 
5.8%
719363
10.1%
613456
7.0%
528741
15.0%
423133
12.1%
316046
8.4%
212468
6.5%
15431
 
2.8%

OST_WEST_KZ
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Memory size1.5 MiB
W
130382 
O
 
11343

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowW
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
W130382
68.0%
O11343
 
5.9%
(Missing)49927
 
26.1%

Length

2021-12-24T13:28:53.148457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:53.259059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
w130382
92.0%
o11343
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing52764
Missing (%)27.5%
Memory size1.5 MiB
2.0
50071 
3.0
49894 
4.0
21336 
1.0
17247 
0.0
 
340

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.050071
26.1%
3.049894
26.0%
4.021336
11.1%
1.017247
 
9.0%
0.0340
 
0.2%
(Missing)52764
27.5%

Length

2021-12-24T13:28:53.368527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:53.467727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.050071
36.1%
3.049894
35.9%
4.021336
15.4%
1.017247
 
12.4%
0.0340
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing52764
Missing (%)27.5%
Memory size1.5 MiB
3.0
61778 
2.0
42938 
4.0
24880 
1.0
8644 
0.0
 
648

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.061778
32.2%
2.042938
22.4%
4.024880
13.0%
1.08644
 
4.5%
0.0648
 
0.3%
(Missing)52764
27.5%

Length

2021-12-24T13:28:53.614705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:53.699321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.061778
44.5%
2.042938
30.9%
4.024880
17.9%
1.08644
 
6.2%
0.0648
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing52764
Missing (%)27.5%
Memory size1.5 MiB
1.0
53775 
2.0
47221 
0.0
23001 
3.0
14891 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.053775
28.1%
2.047221
24.6%
0.023001
12.0%
3.014891
 
7.8%
(Missing)52764
27.5%

Length

2021-12-24T13:28:53.815181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:53.915418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.053775
38.7%
2.047221
34.0%
0.023001
16.6%
3.014891
 
10.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing52764
Missing (%)27.5%
Memory size1.5 MiB
0.0
74829 
1.0
53127 
2.0
10932 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.074829
39.0%
1.053127
27.7%
2.010932
 
5.7%
(Missing)52764
27.5%

Length

2021-12-24T13:28:54.031275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:54.111266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.074829
53.9%
1.053127
38.3%
2.010932
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_BAUMAX
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing52764
Missing (%)27.5%
Memory size1.5 MiB
1.0
106688 
2.0
12315 
5.0
 
10170
4.0
 
4881
3.0
 
4834

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0106688
55.7%
2.012315
 
6.4%
5.010170
 
5.3%
4.04881
 
2.5%
3.04834
 
2.5%
(Missing)52764
27.5%

Length

2021-12-24T13:28:54.211877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:54.292264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0106688
76.8%
2.012315
 
8.9%
5.010170
 
7.3%
4.04881
 
3.5%
3.04834
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing52764
Missing (%)27.5%
Memory size1.5 MiB
3.0
52270 
4.0
38987 
5.0
32798 
2.0
11498 
1.0
 
3335

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row4.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.052270
27.3%
4.038987
20.3%
5.032798
17.1%
2.011498
 
6.0%
1.03335
 
1.7%
(Missing)52764
27.5%

Length

2021-12-24T13:28:54.412860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:54.503485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.052270
37.6%
4.038987
28.1%
5.032798
23.6%
2.011498
 
8.3%
1.03335
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_HHZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing52764
Missing (%)27.5%
Memory size1.5 MiB
3.0
58232 
4.0
38378 
5.0
31053 
2.0
10145 
1.0
 
1080

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row3.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.058232
30.4%
4.038378
20.0%
5.031053
16.2%
2.010145
 
5.3%
1.01080
 
0.6%
(Missing)52764
27.5%

Length

2021-12-24T13:28:54.614064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:54.704588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.058232
41.9%
4.038378
27.6%
5.031053
22.4%
2.010145
 
7.3%
1.01080
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PRAEGENDE_JUGENDJAHRE
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.248272911
Minimum0
Maximum15
Zeros48487
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:54.815179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile11
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.807670852
Coefficient of variation (CV)0.8962867809
Kurtosis-0.08445305812
Mean4.248272911
Median Absolute Deviation (MAD)3
Skewness0.7543637158
Sum814190
Variance14.49835732
MonotonicityNot monotonic
2021-12-24T13:28:54.925761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
048487
25.3%
422216
11.6%
320361
10.6%
517752
 
9.3%
615457
 
8.1%
814910
 
7.8%
211316
 
5.9%
911133
 
5.8%
110405
 
5.4%
116246
 
3.3%
Other values (6)13369
 
7.0%
ValueCountFrequency (%)
048487
25.3%
110405
 
5.4%
211316
 
5.9%
320361
10.6%
422216
11.6%
517752
 
9.3%
615457
 
8.1%
7864
 
0.5%
814910
 
7.8%
911133
 
5.8%
ValueCountFrequency (%)
152550
 
1.3%
143621
 
1.9%
13587
 
0.3%
12714
 
0.4%
116246
3.3%
105033
 
2.6%
911133
5.8%
814910
7.8%
7864
 
0.5%
615457
8.1%

REGIOTYP
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing54260
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean3.814341446
Minimum0
Maximum7
Zeros5804
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:55.046460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.075154504
Coefficient of variation (CV)0.5440400481
Kurtosis-1.252133111
Mean3.814341446
Median Absolute Deviation (MAD)2
Skewness-0.1150760449
Sum524060
Variance4.306266216
MonotonicityNot monotonic
2021-12-24T13:28:55.136884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
628506
14.9%
221866
11.4%
521415
 
11.2%
319764
 
10.3%
117362
 
9.1%
711621
 
6.1%
411054
 
5.8%
05804
 
3.0%
(Missing)54260
28.3%
ValueCountFrequency (%)
05804
 
3.0%
117362
9.1%
221866
11.4%
319764
10.3%
411054
 
5.8%
521415
11.2%
628506
14.9%
711621
6.1%
ValueCountFrequency (%)
711621
6.1%
628506
14.9%
521415
11.2%
411054
 
5.8%
319764
10.3%
221866
11.4%
117362
9.1%
05804
 
3.0%

RELAT_AB
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing50476
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean2.898509662
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:55.247446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.422682617
Coefficient of variation (CV)0.4908324564
Kurtosis-1.152442233
Mean2.898509662
Median Absolute Deviation (MAD)1
Skewness0.127945574
Sum409200
Variance2.024025829
MonotonicityNot monotonic
2021-12-24T13:28:55.347977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
342012
21.9%
133341
17.4%
529068
15.2%
221326
11.1%
415406
 
8.0%
923
 
< 0.1%
(Missing)50476
26.3%
ValueCountFrequency (%)
133341
17.4%
221326
11.1%
342012
21.9%
415406
 
8.0%
529068
15.2%
923
 
< 0.1%
ValueCountFrequency (%)
923
 
< 0.1%
529068
15.2%
415406
 
8.0%
342012
21.9%
221326
11.1%
133341
17.4%

RETOURTYP_BK_S
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
3.0
83297 
5.0
70985 
4.0
15653 
2.0
14366 
1.0
 
4138

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row3.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.083297
43.5%
5.070985
37.0%
4.015653
 
8.2%
2.014366
 
7.5%
1.04138
 
2.2%
(Missing)3213
 
1.7%

Length

2021-12-24T13:28:55.468683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:55.549196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.083297
44.2%
5.070985
37.7%
4.015653
 
8.3%
2.014366
 
7.6%
1.04138
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_KEIN_ANREIZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
1.0
66533 
4.0
58452 
2.0
36421 
3.0
22160 
5.0
 
4873

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.066533
34.7%
4.058452
30.5%
2.036421
19.0%
3.022160
 
11.6%
5.04873
 
2.5%
(Missing)3213
 
1.7%

Length

2021-12-24T13:28:55.659832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:55.740223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.066533
35.3%
4.058452
31.0%
2.036421
19.3%
3.022160
 
11.8%
5.04873
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_SCHNAEPPCHEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing3213
Missing (%)1.7%
Memory size1.5 MiB
5.0
140281 
4.0
23168 
3.0
 
12628
2.0
 
9026
1.0
 
3336

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0140281
73.2%
4.023168
 
12.1%
3.012628
 
6.6%
2.09026
 
4.7%
1.03336
 
1.7%
(Missing)3213
 
1.7%

Length

2021-12-24T13:28:55.848742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:55.958925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0140281
74.4%
4.023168
 
12.3%
3.012628
 
6.7%
2.09026
 
4.8%
1.03336
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_UEBERGROESSE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing44192
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean2.519652787
Minimum0
Maximum5
Zeros1903
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:56.056053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.356234719
Coefficient of variation (CV)0.538262544
Kurtosis-0.7664115865
Mean2.519652787
Median Absolute Deviation (MAD)1
Skewness0.5205391682
Sum371548
Variance1.839372614
MonotonicityNot monotonic
2021-12-24T13:28:56.171912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
245510
23.7%
136913
19.3%
329298
15.3%
520377
10.6%
413459
 
7.0%
01903
 
1.0%
(Missing)44192
23.1%
ValueCountFrequency (%)
01903
 
1.0%
136913
19.3%
245510
23.7%
329298
15.3%
413459
 
7.0%
520377
10.6%
ValueCountFrequency (%)
520377
10.6%
413459
 
7.0%
329298
15.3%
245510
23.7%
136913
19.3%
01903
 
1.0%

SEMIO_DOM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.483835285
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:56.272149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.631940929
Coefficient of variation (CV)0.3639609453
Kurtosis-0.9623202524
Mean4.483835285
Median Absolute Deviation (MAD)1
Skewness-0.3881830332
Sum859336
Variance2.663231195
MonotonicityNot monotonic
2021-12-24T13:28:56.372386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
657788
30.2%
350213
26.2%
541949
21.9%
711750
 
6.1%
211645
 
6.1%
49459
 
4.9%
18848
 
4.6%
ValueCountFrequency (%)
18848
 
4.6%
211645
 
6.1%
350213
26.2%
49459
 
4.9%
541949
21.9%
657788
30.2%
711750
 
6.1%
ValueCountFrequency (%)
711750
 
6.1%
657788
30.2%
541949
21.9%
49459
 
4.9%
350213
26.2%
211645
 
6.1%
18848
 
4.6%

SEMIO_ERL
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.408020788
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:56.463417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.72090874
Coefficient of variation (CV)0.390403953
Kurtosis-1.294525267
Mean4.408020788
Median Absolute Deviation (MAD)1
Skewness0.5593272693
Sum844806
Variance2.961526892
MonotonicityNot monotonic
2021-12-24T13:28:56.562948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
386415
45.1%
747365
24.7%
436480
19.0%
615148
 
7.9%
22698
 
1.4%
52064
 
1.1%
11482
 
0.8%
ValueCountFrequency (%)
11482
 
0.8%
22698
 
1.4%
386415
45.1%
436480
19.0%
52064
 
1.1%
615148
 
7.9%
747365
24.7%
ValueCountFrequency (%)
747365
24.7%
615148
 
7.9%
52064
 
1.1%
436480
19.0%
386415
45.1%
22698
 
1.4%
11482
 
0.8%

SEMIO_FAM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.414026465
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:56.703898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.733128068
Coefficient of variation (CV)0.3926410686
Kurtosis-0.9264928065
Mean4.414026465
Median Absolute Deviation (MAD)1
Skewness-0.5926898725
Sum845957
Variance3.0037329
MonotonicityNot monotonic
2021-12-24T13:28:56.804949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
673209
38.2%
431100
16.2%
526745
 
14.0%
222554
 
11.8%
317959
 
9.4%
115167
 
7.9%
74918
 
2.6%
ValueCountFrequency (%)
115167
 
7.9%
222554
 
11.8%
317959
 
9.4%
431100
16.2%
526745
 
14.0%
673209
38.2%
74918
 
2.6%
ValueCountFrequency (%)
74918
 
2.6%
673209
38.2%
526745
 
14.0%
431100
16.2%
317959
 
9.4%
222554
 
11.8%
115167
 
7.9%

SEMIO_KAEM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.187245633
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:56.905488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.872047112
Coefficient of variation (CV)0.4470831846
Kurtosis-1.363369895
Mean4.187245633
Median Absolute Deviation (MAD)2
Skewness-0.1802487997
Sum802494
Variance3.50456039
MonotonicityNot monotonic
2021-12-24T13:28:56.993917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
666528
34.7%
352725
27.5%
220042
 
10.5%
118025
 
9.4%
516000
 
8.3%
711238
 
5.9%
47094
 
3.7%
ValueCountFrequency (%)
118025
 
9.4%
220042
 
10.5%
352725
27.5%
47094
 
3.7%
516000
 
8.3%
666528
34.7%
711238
 
5.9%
ValueCountFrequency (%)
711238
 
5.9%
666528
34.7%
516000
 
8.3%
47094
 
3.7%
352725
27.5%
220042
 
10.5%
118025
 
9.4%

SEMIO_KRIT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.674535095
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:57.106596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.041058635
Coefficient of variation (CV)0.4366335033
Kurtosis-1.297746471
Mean4.674535095
Median Absolute Deviation (MAD)2
Skewness-0.2053341751
Sum895884
Variance4.16592035
MonotonicityNot monotonic
2021-12-24T13:28:57.195008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
764190
33.5%
360179
31.4%
519500
 
10.2%
117475
 
9.1%
616320
 
8.5%
412573
 
6.6%
21415
 
0.7%
ValueCountFrequency (%)
117475
 
9.1%
21415
 
0.7%
360179
31.4%
412573
 
6.6%
519500
 
10.2%
616320
 
8.5%
764190
33.5%
ValueCountFrequency (%)
764190
33.5%
616320
 
8.5%
519500
 
10.2%
412573
 
6.6%
360179
31.4%
21415
 
0.7%
117475
 
9.1%

SEMIO_KULT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.682497443
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:57.307647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.573090127
Coefficient of variation (CV)0.4271802361
Kurtosis-0.6129899191
Mean3.682497443
Median Absolute Deviation (MAD)1
Skewness-0.00719480503
Sum705758
Variance2.474612548
MonotonicityNot monotonic
2021-12-24T13:28:57.408176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
357410
30.0%
443919
22.9%
525875
13.5%
125732
13.4%
625367
13.2%
28580
 
4.5%
74769
 
2.5%
ValueCountFrequency (%)
125732
13.4%
28580
 
4.5%
357410
30.0%
443919
22.9%
525875
13.5%
625367
13.2%
74769
 
2.5%
ValueCountFrequency (%)
74769
 
2.5%
625367
13.2%
525875
13.5%
443919
22.9%
357410
30.0%
28580
 
4.5%
125732
13.4%

SEMIO_LUST
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.366476739
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:57.518804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.443103117
Coefficient of variation (CV)0.2689107188
Kurtosis1.079596428
Mean5.366476739
Median Absolute Deviation (MAD)1
Skewness-0.9070567185
Sum1028496
Variance2.082546607
MonotonicityNot monotonic
2021-12-24T13:28:57.607275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
574598
38.9%
757681
30.1%
424359
 
12.7%
621952
 
11.5%
16302
 
3.3%
23991
 
2.1%
32769
 
1.4%
ValueCountFrequency (%)
16302
 
3.3%
23991
 
2.1%
32769
 
1.4%
424359
 
12.7%
574598
38.9%
621952
 
11.5%
757681
30.1%
ValueCountFrequency (%)
757681
30.1%
621952
 
11.5%
574598
38.9%
424359
 
12.7%
32769
 
1.4%
23991
 
2.1%
16302
 
3.3%

SEMIO_MAT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.883163233
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:57.709883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.840130675
Coefficient of variation (CV)0.4738741497
Kurtosis-1.219700634
Mean3.883163233
Median Absolute Deviation (MAD)2
Skewness-0.3838452685
Sum744216
Variance3.386080903
MonotonicityNot monotonic
2021-12-24T13:28:57.800323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
553658
28.0%
637054
19.3%
135722
18.6%
427015
14.1%
219913
 
10.4%
314509
 
7.6%
73781
 
2.0%
ValueCountFrequency (%)
135722
18.6%
219913
 
10.4%
314509
 
7.6%
427015
14.1%
553658
28.0%
637054
19.3%
73781
 
2.0%
ValueCountFrequency (%)
73781
 
2.0%
637054
19.3%
553658
28.0%
427015
14.1%
314509
 
7.6%
219913
 
10.4%
135722
18.6%

SEMIO_PFLICHT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.528254336
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:57.900868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.493916302
Coefficient of variation (CV)0.4234151395
Kurtosis-0.8635096163
Mean3.528254336
Median Absolute Deviation (MAD)1
Skewness-0.1434945519
Sum676197
Variance2.231785917
MonotonicityNot monotonic
2021-12-24T13:28:57.991395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
557209
29.9%
440094
20.9%
233245
17.3%
331720
16.6%
122394
 
11.7%
73792
 
2.0%
63198
 
1.7%
ValueCountFrequency (%)
122394
 
11.7%
233245
17.3%
331720
16.6%
440094
20.9%
557209
29.9%
63198
 
1.7%
73792
 
2.0%
ValueCountFrequency (%)
73792
 
2.0%
63198
 
1.7%
557209
29.9%
440094
20.9%
331720
16.6%
233245
17.3%
122394
 
11.7%

SEMIO_RAT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.16587878
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:58.099952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.31622125
Coefficient of variation (CV)0.4157522575
Kurtosis-0.2915751031
Mean3.16587878
Median Absolute Deviation (MAD)1
Skewness-0.006427141384
Sum606747
Variance1.732438379
MonotonicityNot monotonic
2021-12-24T13:28:58.202483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
467155
35.0%
344668
23.3%
229833
15.6%
128171
14.7%
516924
 
8.8%
62641
 
1.4%
72260
 
1.2%
ValueCountFrequency (%)
128171
14.7%
229833
15.6%
344668
23.3%
467155
35.0%
516924
 
8.8%
62641
 
1.4%
72260
 
1.2%
ValueCountFrequency (%)
72260
 
1.2%
62641
 
1.4%
516924
 
8.8%
467155
35.0%
344668
23.3%
229833
15.6%
128171
14.7%

SEMIO_REL
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.112787761
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:58.313134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.070958407
Coefficient of variation (CV)0.5035412784
Kurtosis-1.248722527
Mean4.112787761
Median Absolute Deviation (MAD)2
Skewness0.1823009609
Sum788224
Variance4.288868724
MonotonicityNot monotonic
2021-12-24T13:28:58.403599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
751012
26.6%
438209
19.9%
332567
17.0%
229593
15.4%
120695
10.8%
516734
 
8.7%
62842
 
1.5%
ValueCountFrequency (%)
120695
10.8%
229593
15.4%
332567
17.0%
438209
19.9%
516734
 
8.7%
62842
 
1.5%
751012
26.6%
ValueCountFrequency (%)
751012
26.6%
62842
 
1.5%
516734
 
8.7%
438209
19.9%
332567
17.0%
229593
15.4%
120695
10.8%

SEMIO_SOZ
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.74213679
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:58.504100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.718039291
Coefficient of variation (CV)0.4591064912
Kurtosis-1.375940563
Mean3.74213679
Median Absolute Deviation (MAD)1
Skewness0.2940602431
Sum717188
Variance2.951659006
MonotonicityNot monotonic
2021-12-24T13:28:58.604645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
262302
32.5%
647835
25.0%
331542
16.5%
426231
13.7%
513417
 
7.0%
15556
 
2.9%
74769
 
2.5%
ValueCountFrequency (%)
15556
 
2.9%
262302
32.5%
331542
16.5%
426231
13.7%
513417
 
7.0%
647835
25.0%
74769
 
2.5%
ValueCountFrequency (%)
74769
 
2.5%
647835
25.0%
513417
 
7.0%
426231
13.7%
331542
16.5%
262302
32.5%
15556
 
2.9%

SEMIO_TRADV
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.919160771
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:58.703156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.219223944
Coefficient of variation (CV)0.4176624857
Kurtosis0.268519283
Mean2.919160771
Median Absolute Deviation (MAD)1
Skewness0.1016729042
Sum559463
Variance1.486507025
MonotonicityNot monotonic
2021-12-24T13:28:58.795451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
370115
36.6%
454945
28.7%
135257
18.4%
222806
 
11.9%
54769
 
2.5%
72064
 
1.1%
61696
 
0.9%
ValueCountFrequency (%)
135257
18.4%
222806
 
11.9%
370115
36.6%
454945
28.7%
54769
 
2.5%
61696
 
0.9%
72064
 
1.1%
ValueCountFrequency (%)
72064
 
1.1%
61696
 
0.9%
54769
 
2.5%
454945
28.7%
370115
36.6%
222806
 
11.9%
135257
18.4%

SEMIO_VERT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.185278526
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:28:58.895962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.367406672
Coefficient of variation (CV)0.5656509256
Kurtosis-1.539957163
Mean4.185278526
Median Absolute Deviation (MAD)2
Skewness-0.2238432493
Sum802117
Variance5.60461435
MonotonicityNot monotonic
2021-12-24T13:28:58.986460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
150379
26.3%
746549
24.3%
629192
15.2%
525154
13.1%
418787
 
9.8%
214948
 
7.8%
36643
 
3.5%
ValueCountFrequency (%)
150379
26.3%
214948
 
7.8%
36643
 
3.5%
418787
 
9.8%
525154
13.1%
629192
15.2%
746549
24.3%
ValueCountFrequency (%)
746549
24.3%
629192
15.2%
525154
13.1%
418787
 
9.8%
36643
 
3.5%
214948
 
7.8%
150379
26.3%

SHOPPER_TYP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
51034 
-1
48990 
1
37955 
0
30054 
2
23619 

Length

Max length2
Median length1
Mean length1.25561956
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
351034
26.6%
-148990
25.6%
137955
19.8%
030054
15.7%
223619
12.3%

Length

2021-12-24T13:28:59.851273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:28:59.931658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
186945
45.4%
351034
26.6%
030054
 
15.7%
223619
 
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOHO_KZ
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Memory size1.5 MiB
0.0
143625 
1.0
 
1431

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0143625
74.9%
1.01431
 
0.7%
(Missing)46596
 
24.3%

Length

2021-12-24T13:29:00.042317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:00.132902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0143625
99.0%
1.01431
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

STRUKTURTYP
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing50476
Missing (%)26.3%
Memory size1.5 MiB
3.0
99912 
2.0
21506 
1.0
19758 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.099912
52.1%
2.021506
 
11.2%
1.019758
 
10.3%
(Missing)50476
26.3%

Length

2021-12-24T13:29:00.221368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:00.303822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.099912
70.8%
2.021506
 
15.2%
1.019758
 
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TITEL_KZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Memory size1.5 MiB
0.0
142744 
1.0
 
2017
4.0
 
125
3.0
 
111
5.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0142744
74.5%
1.02017
 
1.1%
4.0125
 
0.1%
3.0111
 
0.1%
5.059
 
< 0.1%
(Missing)46596
 
24.3%

Length

2021-12-24T13:29:00.404305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:00.484566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0142744
98.4%
1.02017
 
1.4%
4.0125
 
0.1%
3.0111
 
0.1%
5.059
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UMFELD_ALT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing50448
Missing (%)26.3%
Memory size1.5 MiB
3.0
36961 
4.0
31237 
2.0
26670 
1.0
25439 
5.0
20897 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.036961
19.3%
4.031237
16.3%
2.026670
13.9%
1.025439
13.3%
5.020897
10.9%
(Missing)50448
26.3%

Length

2021-12-24T13:29:00.595142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:00.675557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.036961
26.2%
4.031237
22.1%
2.026670
18.9%
1.025439
18.0%
5.020897
14.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UMFELD_JUNG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing50448
Missing (%)26.3%
Memory size1.5 MiB
5.0
83655 
4.0
33756 
3.0
14668 
2.0
 
5800
1.0
 
3325

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row4.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.083655
43.6%
4.033756
17.6%
3.014668
 
7.7%
2.05800
 
3.0%
1.03325
 
1.7%
(Missing)50448
26.3%

Length

2021-12-24T13:29:00.796243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:00.884734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.083655
59.2%
4.033756
23.9%
3.014668
 
10.4%
2.05800
 
4.1%
1.03325
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UNGLEICHENN_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Memory size1.5 MiB
0.0
132763 
1.0
 
12293

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0132763
69.3%
1.012293
 
6.4%
(Missing)46596
 
24.3%

Length

2021-12-24T13:29:00.997019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:01.077464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0132763
91.5%
1.012293
 
8.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VERDICHTUNGSRAUM
Real number (ℝ≥0)

MISSING
ZEROS

Distinct46
Distinct (%)< 0.1%
Missing50476
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean5.055087267
Minimum0
Maximum45
Zeros65984
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:01.175964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile28
Maximum45
Range45
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.250234572
Coefficient of variation (CV)1.829886228
Kurtosis4.88311212
Mean5.055087267
Median Absolute Deviation (MAD)1
Skewness2.329566347
Sum713657
Variance85.56683964
MonotonicityNot monotonic
2021-12-24T13:29:01.316891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
065984
34.4%
120857
 
10.9%
36799
 
3.5%
66449
 
3.4%
45184
 
2.7%
23732
 
1.9%
73298
 
1.7%
82766
 
1.4%
52063
 
1.1%
241760
 
0.9%
Other values (36)22284
 
11.6%
(Missing)50476
26.3%
ValueCountFrequency (%)
065984
34.4%
120857
 
10.9%
23732
 
1.9%
36799
 
3.5%
45184
 
2.7%
52063
 
1.1%
66449
 
3.4%
73298
 
1.7%
82766
 
1.4%
9768
 
0.4%
ValueCountFrequency (%)
45378
0.2%
44464
0.2%
43145
 
0.1%
42448
0.2%
4171
 
< 0.1%
4059
 
< 0.1%
39196
 
0.1%
38592
0.3%
3764
 
< 0.1%
36638
0.3%

VERS_TYP
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
73620 
2
69042 
-1
48990 

Length

Max length2
Median length1
Mean length1.25561956
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
173620
38.4%
269042
36.0%
-148990
25.6%

Length

2021-12-24T13:29:01.459694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:01.560284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1122610
64.0%
269042
36.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VHA
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean0.8685335319
Minimum0
Maximum5
Zeros74250
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:01.630649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.320530263
Coefficient of variation (CV)1.52041368
Kurtosis3.115032695
Mean0.8685335319
Median Absolute Deviation (MAD)0
Skewness1.971956014
Sum125986
Variance1.743800175
MonotonicityNot monotonic
2021-12-24T13:29:01.731289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
074250
38.7%
152376
27.3%
57001
 
3.7%
45259
 
2.7%
35229
 
2.7%
2941
 
0.5%
(Missing)46596
24.3%
ValueCountFrequency (%)
074250
38.7%
152376
27.3%
2941
 
0.5%
35229
 
2.7%
45259
 
2.7%
57001
 
3.7%
ValueCountFrequency (%)
57001
 
3.7%
45259
 
2.7%
35229
 
2.7%
2941
 
0.5%
152376
27.3%
074250
38.7%

VHN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing54260
Missing (%)28.3%
Memory size1.5 MiB
2.0
43849 
4.0
31906 
3.0
31320 
1.0
24513 
0.0
5804 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.043849
22.9%
4.031906
16.6%
3.031320
16.3%
1.024513
12.8%
0.05804
 
3.0%
(Missing)54260
28.3%

Length

2021-12-24T13:29:01.841977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:01.942526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.043849
31.9%
4.031906
23.2%
3.031320
22.8%
1.024513
17.8%
0.05804
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VK_DHT4A
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)< 0.1%
Missing47871
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean4.374416648
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:02.063265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.924355416
Coefficient of variation (CV)0.6685132331
Kurtosis-1.094468891
Mean4.374416648
Median Absolute Deviation (MAD)3
Skewness0.4529847245
Sum628958
Variance8.551854599
MonotonicityNot monotonic
2021-12-24T13:29:02.163820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
131747
16.6%
221120
11.0%
316768
 
8.7%
714282
 
7.5%
411509
 
6.0%
611365
 
5.9%
510685
 
5.6%
108865
 
4.6%
98842
 
4.6%
88588
 
4.5%
(Missing)47871
25.0%
ValueCountFrequency (%)
131747
16.6%
221120
11.0%
316768
8.7%
411509
 
6.0%
510685
 
5.6%
611365
 
5.9%
714282
7.5%
88588
 
4.5%
98842
 
4.6%
108865
 
4.6%
ValueCountFrequency (%)
1110
 
< 0.1%
108865
4.6%
98842
4.6%
88588
4.5%
714282
7.5%
611365
5.9%
510685
5.6%
411509
6.0%
316768
8.7%
221120
11.0%

VK_DISTANZ
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)< 0.1%
Missing47871
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean4.564768641
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:02.264459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.887034938
Coefficient of variation (CV)0.6324602986
Kurtosis-0.432615138
Mean4.564768641
Median Absolute Deviation (MAD)2
Skewness0.5634636033
Sum656327
Variance8.334970733
MonotonicityNot monotonic
2021-12-24T13:29:02.372966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
127736
14.5%
322378
11.7%
620769
10.8%
415262
 
8.0%
213172
 
6.9%
712612
 
6.6%
89272
 
4.8%
58223
 
4.3%
95905
 
3.1%
103406
 
1.8%
Other values (3)5046
 
2.6%
(Missing)47871
25.0%
ValueCountFrequency (%)
127736
14.5%
213172
6.9%
322378
11.7%
415262
8.0%
58223
 
4.3%
620769
10.8%
712612
6.6%
89272
 
4.8%
95905
 
3.1%
103406
 
1.8%
ValueCountFrequency (%)
13722
 
0.4%
121721
 
0.9%
112603
 
1.4%
103406
 
1.8%
95905
 
3.1%
89272
4.8%
712612
6.6%
620769
10.8%
58223
 
4.3%
415262
8.0%

VK_ZG11
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)< 0.1%
Missing47871
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean3.168867931
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:02.475540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.233515815
Coefficient of variation (CV)0.7048308303
Kurtosis1.753072506
Mean3.168867931
Median Absolute Deviation (MAD)1
Skewness1.381714244
Sum455623
Variance4.988592896
MonotonicityNot monotonic
2021-12-24T13:29:02.594196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
136846
19.2%
233192
17.3%
325412
13.3%
417012
 
8.9%
512225
 
6.4%
66625
 
3.5%
73522
 
1.8%
83113
 
1.6%
92582
 
1.3%
111918
 
1.0%
(Missing)47871
25.0%
ValueCountFrequency (%)
136846
19.2%
233192
17.3%
325412
13.3%
417012
8.9%
512225
 
6.4%
66625
 
3.5%
73522
 
1.8%
83113
 
1.6%
92582
 
1.3%
101334
 
0.7%
ValueCountFrequency (%)
111918
 
1.0%
101334
 
0.7%
92582
 
1.3%
83113
 
1.6%
73522
 
1.8%
66625
 
3.5%
512225
 
6.4%
417012
8.9%
325412
13.3%
233192
17.3%

W_KEIT_KIND_HH
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing53742
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean4.152715539
Minimum0
Maximum6
Zeros3195
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:02.704816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.974374585
Coefficient of variation (CV)0.4754418082
Kurtosis-1.322027583
Mean4.152715539
Median Absolute Deviation (MAD)1
Skewness-0.4630565154
Sum572701
Variance3.898155001
MonotonicityNot monotonic
2021-12-24T13:29:02.807354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
663841
33.3%
225596
13.4%
414515
 
7.6%
112647
 
6.6%
311412
 
6.0%
56704
 
3.5%
03195
 
1.7%
(Missing)53742
28.0%
ValueCountFrequency (%)
03195
 
1.7%
112647
 
6.6%
225596
13.4%
311412
 
6.0%
414515
 
7.6%
56704
 
3.5%
663841
33.3%
ValueCountFrequency (%)
663841
33.3%
56704
 
3.5%
414515
 
7.6%
311412
 
6.0%
225596
13.4%
112647
 
6.6%
03195
 
1.7%

WOHNDAUER_2008
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing46596
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean8.646371057
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:02.914929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median9
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.154000554
Coefficient of variation (CV)0.1334664619
Kurtosis13.26062379
Mean8.646371057
Median Absolute Deviation (MAD)0
Skewness-3.689679911
Sum1254208
Variance1.331717278
MonotonicityNot monotonic
2021-12-24T13:29:03.005489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9127266
66.4%
86924
 
3.6%
42476
 
1.3%
62351
 
1.2%
32115
 
1.1%
71993
 
1.0%
51784
 
0.9%
198
 
0.1%
249
 
< 0.1%
(Missing)46596
 
24.3%
ValueCountFrequency (%)
198
 
0.1%
249
 
< 0.1%
32115
 
1.1%
42476
 
1.3%
51784
 
0.9%
62351
 
1.2%
71993
 
1.0%
86924
 
3.6%
9127266
66.4%
ValueCountFrequency (%)
9127266
66.4%
86924
 
3.6%
71993
 
1.0%
62351
 
1.2%
51784
 
0.9%
42476
 
1.3%
32115
 
1.1%
249
 
< 0.1%
198
 
0.1%

WOHNLAGE
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)< 0.1%
Missing49927
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean3.723132828
Minimum0
Maximum8
Zeros1107
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:03.136290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.095540209
Coefficient of variation (CV)0.5628432574
Kurtosis-0.9042610323
Mean3.723132828
Median Absolute Deviation (MAD)1
Skewness0.5797956838
Sum527661
Variance4.391288766
MonotonicityNot monotonic
2021-12-24T13:29:03.246905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
345074
23.5%
731328
16.3%
224427
12.7%
116567
 
8.6%
415096
 
7.9%
55890
 
3.1%
82236
 
1.2%
01107
 
0.6%
(Missing)49927
26.1%
ValueCountFrequency (%)
01107
 
0.6%
116567
 
8.6%
224427
12.7%
345074
23.5%
415096
 
7.9%
55890
 
3.1%
731328
16.3%
82236
 
1.2%
ValueCountFrequency (%)
82236
 
1.2%
731328
16.3%
55890
 
3.1%
415096
 
7.9%
345074
23.5%
224427
12.7%
116567
 
8.6%
01107
 
0.6%

ZABEOTYP
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.576805877
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-12-24T13:29:03.357556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.168485772
Coefficient of variation (CV)0.4534628635
Kurtosis0.4729641663
Mean2.576805877
Median Absolute Deviation (MAD)0
Skewness0.2822703633
Sum493850
Variance1.365358999
MonotonicityNot monotonic
2021-12-24T13:29:03.448173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3108682
56.7%
154347
28.4%
414809
 
7.7%
26899
 
3.6%
65848
 
3.1%
51067
 
0.6%
ValueCountFrequency (%)
154347
28.4%
26899
 
3.6%
3108682
56.7%
414809
 
7.7%
51067
 
0.6%
65848
 
3.1%
ValueCountFrequency (%)
65848
 
3.1%
51067
 
0.6%
414809
 
7.7%
3108682
56.7%
26899
 
3.6%
154347
28.4%

PRODUCT_GROUP
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
COSMETIC_AND_FOOD
100860 
FOOD
47382 
COSMETIC
43410 

Length

Max length17
Median length17
Mean length11.74747981
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOSMETIC_AND_FOOD
2nd rowFOOD
3rd rowCOSMETIC_AND_FOOD
4th rowCOSMETIC
5th rowFOOD

Common Values

ValueCountFrequency (%)
COSMETIC_AND_FOOD100860
52.6%
FOOD47382
24.7%
COSMETIC43410
22.7%

Length

2021-12-24T13:29:03.568953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:03.639350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
cosmetic_and_food100860
52.6%
food47382
24.7%
cosmetic43410
22.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CUSTOMER_GROUP
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
MULTI_BUYER
132238 
SINGLE_BUYER
59414 

Length

Max length12
Median length11
Mean length11.31000981
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMULTI_BUYER
2nd rowSINGLE_BUYER
3rd rowMULTI_BUYER
4th rowMULTI_BUYER
5th rowMULTI_BUYER

Common Values

ValueCountFrequency (%)
MULTI_BUYER132238
69.0%
SINGLE_BUYER59414
31.0%

Length

2021-12-24T13:29:03.740136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:03.820591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
multi_buyer132238
69.0%
single_buyer59414
31.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ONLINE_PURCHASE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
174356 
1
 
17296

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0174356
91.0%
117296
 
9.0%

Length

2021-12-24T13:29:03.901057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:03.981460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0174356
91.0%
117296
 
9.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ANREDE_KZ
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
119508 
2
72144 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1119508
62.4%
272144
37.6%

Length

2021-12-24T13:29:04.051964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:04.132596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1119508
62.4%
272144
37.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ALTERSKATEGORIE_GROB
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
85834 
3
58364 
1
28387 
2
18827 
9
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row3

Common Values

ValueCountFrequency (%)
485834
44.8%
358364
30.5%
128387
 
14.8%
218827
 
9.8%
9240
 
0.1%

Length

2021-12-24T13:29:04.213124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T13:29:04.301671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
485834
44.8%
358364
30.5%
128387
 
14.8%
218827
 
9.8%
9240
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sample

First rows

LNRAGER_TYPAKT_DAT_KLALTER_HHALTER_KIND1ALTER_KIND2ALTER_KIND3ALTER_KIND4ALTERSKATEGORIE_FEINANZ_HAUSHALTE_AKTIVANZ_HH_TITELANZ_KINDERANZ_PERSONENANZ_STATISTISCHE_HAUSHALTEANZ_TITELARBEITBALLRAUMCAMEO_DEU_2015CAMEO_DEUG_2015CAMEO_INTL_2015CJT_GESAMTTYPCJT_KATALOGNUTZERCJT_TYP_1CJT_TYP_2CJT_TYP_3CJT_TYP_4CJT_TYP_5CJT_TYP_6D19_BANKEN_ANZ_12D19_BANKEN_ANZ_24D19_BANKEN_DATUMD19_BANKEN_DIREKTD19_BANKEN_GROSSD19_BANKEN_LOKALD19_BANKEN_OFFLINE_DATUMD19_BANKEN_ONLINE_DATUMD19_BANKEN_ONLINE_QUOTE_12D19_BANKEN_RESTD19_BEKLEIDUNG_GEHD19_BEKLEIDUNG_RESTD19_BILDUNGD19_BIO_OEKOD19_BUCH_CDD19_DIGIT_SERVD19_DROGERIEARTIKELD19_ENERGIED19_FREIZEITD19_GARTEND19_GESAMT_ANZ_12D19_GESAMT_ANZ_24D19_GESAMT_DATUMD19_GESAMT_OFFLINE_DATUMD19_GESAMT_ONLINE_DATUMD19_GESAMT_ONLINE_QUOTE_12D19_HANDWERKD19_HAUS_DEKOD19_KINDERARTIKELD19_KONSUMTYPD19_KONSUMTYP_MAXD19_KOSMETIKD19_LEBENSMITTELD19_LETZTER_KAUF_BRANCHED19_LOTTOD19_NAHRUNGSERGAENZUNGD19_RATGEBERD19_REISEND19_SAMMELARTIKELD19_SCHUHED19_SONSTIGED19_SOZIALESD19_TECHNIKD19_TELKO_ANZ_12D19_TELKO_ANZ_24D19_TELKO_DATUMD19_TELKO_MOBILED19_TELKO_OFFLINE_DATUMD19_TELKO_ONLINE_DATUMD19_TELKO_ONLINE_QUOTE_12D19_TELKO_RESTD19_TIERARTIKELD19_VERSAND_ANZ_12D19_VERSAND_ANZ_24D19_VERSAND_DATUMD19_VERSAND_OFFLINE_DATUMD19_VERSAND_ONLINE_DATUMD19_VERSAND_ONLINE_QUOTE_12D19_VERSAND_RESTD19_VERSI_ANZ_12D19_VERSI_ANZ_24D19_VERSI_DATUMD19_VERSI_OFFLINE_DATUMD19_VERSI_ONLINE_DATUMD19_VERSI_ONLINE_QUOTE_12D19_VERSICHERUNGEND19_VOLLSORTIMENTD19_WEIN_FEINKOSTDSL_FLAGEINGEFUEGT_AMEINGEZOGENAM_HH_JAHREWDICHTEEXTSEL992FINANZ_ANLEGERFINANZ_HAUSBAUERFINANZ_MINIMALISTFINANZ_SPARERFINANZ_UNAUFFAELLIGERFINANZ_VORSORGERFINANZTYPFIRMENDICHTEGEBAEUDETYPGEBAEUDETYP_RASTERGEBURTSJAHRGEMEINDETYPGFK_URLAUBERTYPGREEN_AVANTGARDEHEALTH_TYPHH_DELTA_FLAGHH_EINKOMMEN_SCOREINNENSTADTKBA05_ALTER1KBA05_ALTER2KBA05_ALTER3KBA05_ALTER4KBA05_ANHANGKBA05_ANTG1KBA05_ANTG2KBA05_ANTG3KBA05_ANTG4KBA05_AUTOQUOTKBA05_BAUMAXKBA05_CCM1KBA05_CCM2KBA05_CCM3KBA05_CCM4KBA05_DIESELKBA05_FRAUKBA05_GBZKBA05_HERST1KBA05_HERST2KBA05_HERST3KBA05_HERST4KBA05_HERST5KBA05_HERSTTEMPKBA05_KRSAQUOTKBA05_KRSHERST1KBA05_KRSHERST2KBA05_KRSHERST3KBA05_KRSKLEINKBA05_KRSOBERKBA05_KRSVANKBA05_KRSZULKBA05_KW1KBA05_KW2KBA05_KW3KBA05_MAXAHKBA05_MAXBJKBA05_MAXHERSTKBA05_MAXSEGKBA05_MAXVORBKBA05_MOD1KBA05_MOD2KBA05_MOD3KBA05_MOD4KBA05_MOD8KBA05_MODTEMPKBA05_MOTORKBA05_MOTRADKBA05_SEG1KBA05_SEG10KBA05_SEG2KBA05_SEG3KBA05_SEG4KBA05_SEG5KBA05_SEG6KBA05_SEG7KBA05_SEG8KBA05_SEG9KBA05_VORB0KBA05_VORB1KBA05_VORB2KBA05_ZUL1KBA05_ZUL2KBA05_ZUL3KBA05_ZUL4KBA13_ALTERHALTER_30KBA13_ALTERHALTER_45KBA13_ALTERHALTER_60KBA13_ALTERHALTER_61KBA13_ANTG1KBA13_ANTG2KBA13_ANTG3KBA13_ANTG4KBA13_ANZAHL_PKWKBA13_AUDIKBA13_AUTOQUOTEKBA13_BAUMAXKBA13_BJ_1999KBA13_BJ_2000KBA13_BJ_2004KBA13_BJ_2006KBA13_BJ_2008KBA13_BJ_2009KBA13_BMWKBA13_CCM_0_1400KBA13_CCM_1000KBA13_CCM_1200KBA13_CCM_1400KBA13_CCM_1401_2500KBA13_CCM_1500KBA13_CCM_1600KBA13_CCM_1800KBA13_CCM_2000KBA13_CCM_2500KBA13_CCM_2501KBA13_CCM_3000KBA13_CCM_3001KBA13_FAB_ASIENKBA13_FAB_SONSTIGEKBA13_FIATKBA13_FORDKBA13_GBZKBA13_HALTER_20KBA13_HALTER_25KBA13_HALTER_30KBA13_HALTER_35KBA13_HALTER_40KBA13_HALTER_45KBA13_HALTER_50KBA13_HALTER_55KBA13_HALTER_60KBA13_HALTER_65KBA13_HALTER_66KBA13_HERST_ASIENKBA13_HERST_AUDI_VWKBA13_HERST_BMW_BENZKBA13_HERST_EUROPAKBA13_HERST_FORD_OPELKBA13_HERST_SONSTKBA13_HHZKBA13_KMH_0_140KBA13_KMH_110KBA13_KMH_140KBA13_KMH_140_210KBA13_KMH_180KBA13_KMH_210KBA13_KMH_211KBA13_KMH_250KBA13_KMH_251KBA13_KRSAQUOTKBA13_KRSHERST_AUDI_VWKBA13_KRSHERST_BMW_BENZKBA13_KRSHERST_FORD_OPELKBA13_KRSSEG_KLEINKBA13_KRSSEG_OBERKBA13_KRSSEG_VANKBA13_KRSZUL_NEUKBA13_KW_0_60KBA13_KW_110KBA13_KW_120KBA13_KW_121KBA13_KW_30KBA13_KW_40KBA13_KW_50KBA13_KW_60KBA13_KW_61_120KBA13_KW_70KBA13_KW_80KBA13_KW_90KBA13_MAZDAKBA13_MERCEDESKBA13_MOTORKBA13_NISSANKBA13_OPELKBA13_PEUGEOTKBA13_RENAULTKBA13_SEG_GELAENDEWAGENKBA13_SEG_GROSSRAUMVANSKBA13_SEG_KLEINSTKBA13_SEG_KLEINWAGENKBA13_SEG_KOMPAKTKLASSEKBA13_SEG_MINIVANSKBA13_SEG_MINIWAGENKBA13_SEG_MITTELKLASSEKBA13_SEG_OBEREMITTELKLASSEKBA13_SEG_OBERKLASSEKBA13_SEG_SONSTIGEKBA13_SEG_SPORTWAGENKBA13_SEG_UTILITIESKBA13_SEG_VANKBA13_SEG_WOHNMOBILEKBA13_SITZE_4KBA13_SITZE_5KBA13_SITZE_6KBA13_TOYOTAKBA13_VORB_0KBA13_VORB_1KBA13_VORB_1_2KBA13_VORB_2KBA13_VORB_3KBA13_VWKK_KUNDENTYPKKKKOMBIALTERKONSUMNAEHEKONSUMZELLELP_FAMILIE_FEINLP_FAMILIE_GROBLP_LEBENSPHASE_FEINLP_LEBENSPHASE_GROBLP_STATUS_FEINLP_STATUS_GROBMIN_GEBAEUDEJAHRMOBI_RASTERMOBI_REGIONATIONALITAET_KZONLINE_AFFINITAETORTSGR_KLS9OST_WEST_KZPLZ8_ANTG1PLZ8_ANTG2PLZ8_ANTG3PLZ8_ANTG4PLZ8_BAUMAXPLZ8_GBZPLZ8_HHZPRAEGENDE_JUGENDJAHREREGIOTYPRELAT_ABRETOURTYP_BK_SRT_KEIN_ANREIZRT_SCHNAEPPCHENRT_UEBERGROESSESEMIO_DOMSEMIO_ERLSEMIO_FAMSEMIO_KAEMSEMIO_KRITSEMIO_KULTSEMIO_LUSTSEMIO_MATSEMIO_PFLICHTSEMIO_RATSEMIO_RELSEMIO_SOZSEMIO_TRADVSEMIO_VERTSHOPPER_TYPSOHO_KZSTRUKTURTYPTITEL_KZUMFELD_ALTUMFELD_JUNGUNGLEICHENN_FLAGVERDICHTUNGSRAUMVERS_TYPVHAVHNVK_DHT4AVK_DISTANZVK_ZG11W_KEIT_KIND_HHWOHNDAUER_2008WOHNLAGEZABEOTYPPRODUCT_GROUPCUSTOMER_GROUPONLINE_PURCHASEA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314387311.08.0NaNNaNNaNNaN8.00.0NaN0.00.01.00.01.07.04C4242.05.01.01.05.05.05.05.0001000010100.0000006000000166100.00003.0200D19_NAHRUNGSERGAENZUNG0.05000001.000010010100.0000166100.0000910100.06601.01992-02-10 00:00:001997.01.010.022511564.02.04.0040.010.002NaN4.07.02.03.03.03.03.03.00.00.00.03.01.03.04.02.00.03.02.04.03.01.03.05.03.03.03.04.02.02.02.01.02.03.03.04.00.03.04.04.01.03.00.03.05.00.01.04.02.01.03.02.04.03.04.00.00.00.02.00.03.03.02.02.03.04.04.03.04.03.02.02.02.00.00.0755.02.04.01.03.02.03.04.03.02.04.02.02.00.02.04.03.04.03.04.03.03.03.03.04.03.01.03.05.04.04.03.04.04.04.04.03.03.01.02.04.01.04.05.02.03.03.03.02.03.03.03.04.03.03.01.04.03.05.01.01.03.03.01.02.03.03.03.02.02.02.02.04.02.05.02.05.04.03.02.01.05.02.05.03.01.02.03.04.02.03.02.00.04.04.03.04.02.03.03.03.05.04.03.02.02.03.01.0NaN3.042.00.00.00.00.00.09.04.01992.03.04.012.03.0W3.02.01.00.01.04.03.016.01.03.01.05.02.03353345433364700.01.00.03.04.00.00.010.02.06.04.02.0NaN9.07.01COSMETICMULTI_BUYER014
4143874-11.020.0NaNNaNNaNNaN14.07.00.00.04.07.00.03.03.07B7416.04.03.03.03.04.03.03.01235031070.0006002040603518110.00601.0405D19_SCHUHE0.00606361.0601751090.0603518110.03001010100.00001.01992-02-12 00:00:001997.04.0NaN42315422.03.03.0196022.02.0031.06.04.02.04.04.01.00.00.03.02.00.03.00.02.05.02.00.02.03.03.01.04.03.02.03.01.02.02.04.03.01.02.01.01.03.04.00.03.01.02.02.03.01.03.03.01.01.04.02.00.00.01.02.04.03.01.00.01.00.00.02.04.03.03.03.03.01.01.03.03.04.02.04.02.01.0513.02.03.02.04.03.03.03.05.01.03.03.03.03.04.02.01.03.00.02.00.03.04.01.04.03.03.04.03.02.02.01.02.02.05.04.03.03.03.04.03.02.03.04.04.03.03.03.01.04.03.04.02.03.03.01.03.03.02.03.02.02.02.02.04.02.01.03.01.03.04.04.01.03.00.03.02.03.04.04.04.03.04.03.04.04.05.03.04.03.02.03.01.03.03.03.04.03.04.02.03.03.03.03.03.03.00.02.02.04.031.01.010.05.031.010.01.01.01992.01.03.015.05.0W2.04.02.01.02.03.03.087.01.05.04.03.05.05452356655444510.03.00.02.04.00.01.020.04.03.05.04.02.09.03.01FOODMULTI_BUYER013
514388811.011.0NaNNaNNaNNaN10.01.00.00.02.01.00.03.07.05D5344.03.01.01.05.05.05.05.0001000010100.0006005000000166100.06002.0200D19_BUCH_CD7.00000061.060010010100.0000166100.00001010100.00661.01992-02-10 00:00:001994.05.048.013512553.01.04.0012.011.0130.01.08.00.02.03.05.00.02.02.01.00.03.00.03.02.03.04.01.05.03.03.04.01.03.03.02.04.03.04.02.02.03.02.03.03.02.04.05.04.02.04.01.04.03.01.02.02.01.04.01.02.03.02.02.03.04.01.01.01.02.05.01.01.02.03.04.03.02.05.03.03.02.03.02.01.01167.03.02.01.03.02.03.03.04.05.04.02.00.02.03.03.04.03.02.03.05.05.04.04.03.02.03.02.05.02.02.03.04.04.04.04.02.02.03.03.03.04.05.03.01.02.05.03.01.03.01.02.03.05.05.01.05.03.05.02.02.01.02.03.02.03.05.04.01.02.02.03.03.02.00.02.02.05.01.03.01.04.03.02.03.02.02.03.02.04.04.05.03.02.03.03.03.03.03.02.05.04.04.03.03.02.01.04.0NaN2.042.00.02.02.017.05.07.03.01992.02.03.013.07.0W2.03.02.01.01.05.05.043.05.03.01.05.02.05443566132464710.03.00.02.05.00.024.025.04.01.02.01.06.09.01.02COSMETIC_AND_FOODMULTI_BUYER013
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714391011.010.0NaNNaNNaNNaN9.01.00.00.02.01.00.03.05.01D1152.05.01.01.05.05.05.05.0001000010100.0060000000001158100.00003.0200D19_SONSTIGE7.00000631.0000969100.0001188100.00001010100.00001.01992-02-10 00:00:001994.03.055.012512554.01.04.0193830.08.0110.01.05.01.02.01.05.01.03.02.00.00.04.01.03.02.04.01.03.02.04.04.04.02.03.01.02.03.04.04.02.02.02.02.03.02.04.02.05.04.02.03.01.02.03.03.03.02.02.03.00.01.02.02.03.04.02.00.00.00.02.05.01.03.02.03.03.04.02.02.03.04.02.03.01.01.0481.03.03.01.01.01.02.03.05.05.05.00.01.00.01.04.01.04.03.04.05.03.03.01.03.03.03.04.03.04.03.02.02.01.03.03.04.03.04.03.03.03.04.02.03.03.03.01.01.01.01.02.03.05.05.01.03.03.05.02.02.02.02.03.01.04.05.03.01.01.00.01.05.00.04.00.04.03.02.02.02.03.02.04.03.03.03.01.05.03.03.03.05.03.03.03.04.03.04.02.03.02.05.03.02.02.00.04.05.01.043.00.02.02.020.05.010.05.01992.04.04.013.04.0W3.03.01.01.01.03.03.041.03.05.01.05.02.03341347611561730.03.00.01.04.00.00.011.03.01.02.01.06.09.03.03FOODSINGLE_BUYER014
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Last rows

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